eBay:iPads:: sold classification:: dataclns
bdanalytics
Date: (Sun) Jul 19, 2015
Introduction:
Data: Source: Training: https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTrain.csv
New: https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTest.csv
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
Regression results: First run:
Classification results: template: prdline.my == “Unknown” -> 296 Low.cor.X.glm: Leaderboard: 0.83458 newobs_tbl=[N=471, Y=327]; submit_filename=template_Final_glm_submit.csv OOB_conf_mtrx=[YN=125, NY=76]=201; max.Accuracy.OOB=0.7710; opt.prob.threshold.OOB=0.6 startprice=100.00; biddable=95.42; productline=49.22; D.T.like=29.75; D.T.use=26.32; D.T.box=21.53;
prdline: -> Worse than template prdline.my == “Unknown” -> 285 All.X.no.rnorm.rf: Leaderboard: 0.82649 newobs_tbl=[N=485, Y=313]; submit_filename=prdline_Final_rf_submit.csv OOB_conf_mtrx=[YN=119, NY=80]=199; max.Accuracy.OOB=0.8339; opt.prob.threshold.OOB=0.5 startprice=100.00; biddable=84.25; D.sum.TfIdf=7.28; D.T.use=4.26; D.T.veri=2.78; D.T.scratch=1.99; D.T.box=; D.T.like=; Low.cor.X.glm: Leaderboard: 0.81234 newobs_tbl=[N=471, Y=327]; submit_filename=prdline_Low_cor_X_glm_submit.csv OOB_conf_mtrx=[YN=125, NY=74]=199; max.Accuracy.OOB=0.8205; opt.prob.threshold.OOB=0.6 startprice=100.00; biddable=96.07; prdline.my=51.37; D.T.like=29.39; D.T.use=25.43; D.T.box=22.27; D.T.veri=; D.T.scratch=;
oobssmpl: -> Low.cor.X.glm: Leaderboard: 0.83402 newobs_tbl=[N=440, Y=358]; submit_filename=oobsmpl_Final_glm_submit OOB_conf_mtrx=[YN=114, NY=84]=198; max.Accuracy.OOB=0.7780; opt.prob.threshold.OOB=0.5 startprice=100.00; biddable=93.87; prdline.my=60.48; D.sum.TfIdf=; D.T.condition=8.69; D.T.screen=7.96; D.T.use=7.50; D.T.veri=; D.T.scratch=;
category: -> Low.cor.X.glm: Leaderboard: 0.82381 newobs_tbl=[N=470, Y=328]; submit_filename=category_Final_glm_submit OOB_conf_mtrx=[YN=119, NY=57]=176; max.Accuracy.OOB=0.8011; opt.prob.threshold.OOB=0.6 startprice=100.00; biddable=79.19; prdline.my=55.22; D.sum.TfIdf=; D.T.ipad=27.05; D.T.like=21.44; D.T.box=20.67; D.T.condition=; D.T.screen=;
dataclns: -> All.X.no.rnorm.rf: Leaderboard: 0.82211 newobs_tbl=[N=485, Y=313]; submit_filename=dataclns_Final_rf_submit OOB_conf_mtrx=[YN=104, NY=75]=179; max.Accuracy.OOB=0.7977; opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=65.85; prdline.my=7.74; D.sum.TfIdf=; D.T.use=2.01; D.T.condition=1.87; D.T.veri=1.62; D.T.ipad=; D.T.like=; Low.cor.X.glm: Leaderboard: 0.79264 newobs_tbl=[N=460, Y=338]; submit_filename=dataclns_Low_cor_X_glm_submit OOB_conf_mtrx=[YN=113, NY=74]=187; max.Accuracy.OOB=0.7977; opt.prob.threshold.OOB=0.5 -> different from prev run of 0.6 biddable=100.00; startprice.log=91.85; prdline.my=38.34; D.sum.TfIdf=; D.T.ipad=29.92; D.T.box=27.76; D.T.work=25.79; D.T.use=; D.T.condition=;
Prediction Accuracy Enhancement Options:
- import.data chunk:
- which obs should be in fit vs. OOB (currently dirty.0 vs .1 is split 50%)
- inspect.data chunk:
- For date variables
- Appropriate factors ?
- Different / More last* features ?
- For date variables
- scrub.data chunk:
- transform.data chunk:
- derive features from multiple features
- manage.missing.data chunk:
- Not fill missing vars
- Fill missing numerics with a different algorithm
- Fill missing chars with data based on clusters
- extract.features chunk:
- Text variables: move to date extraction chunk ???
- Mine acronyms
- Mine places
- Text variables: move to date extraction chunk ???
- Review set_global_options chunk after features are finalized
[](.png)
Potential next steps include:
- Organization:
- Categorize by chunk
- Priority criteria:
- Ease of change
- Impacts report
- Cleans innards
- Bug report
- all chunks:
- at chunk-end rm(!glb_)
- manage.missing.data chunk:
- cleaner way to manage re-splitting of training vs. new entity
- extract.features chunk:
- Add n-grams for glb_txt_vars
- “RTextTools”, “tau”, “RWeka”, and “textcat” packages
- Convert user-specified mutate code to config specs
- Add n-grams for glb_txt_vars
- fit.models chunk:
- Prediction accuracy scatter graph:
- Add tiles (raw vs. PCA)
- Use shiny for drop-down of “important” features
Use plot.ly for interactive plots ?
- Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
- move model_type parameter to myfit_mdl before indep_vars_vctr (keep all model_* together)
- create a custom model for rpart that has minbucket as a tuning parameter
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
- Probability handling for multinomials vs. desired binomial outcome
- ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
extensions toward multiclass classification are scheduled for the next release
- Skip trControl.method=“cv” for dummy classifier ?
- Add custom model to caret for a dummy (baseline) classifier (binomial & multinomial) that generates proba/outcomes which mimics the freq distribution of glb_rsp_var values; Right now glb_dmy_glm_mdl always generates most frequent outcome in training data
glm_dmy_mdl should use the same method as glm_sel_mdl until custom dummy classifer is implemented
- fit.all.training chunk:
- myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
- Compare glb_sel_mdl vs. glb_fin_mdl:
- varImp
- Prediction differences (shd be minimal ?)
- Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
- Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
- Parameterize glb_analytics_pn
- Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
- Replicate myfit_mdl_classification features in myfit_mdl_regression
- Do non-glm methods handle interaction terms ?
- f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
- Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
- Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
- Incorporate code chunks in print_sessionInfo
- Test against
- projects in github.com/bdanalytics
- lectures in jhu-datascience track
Analysis:
rm(list=ls())
set.seed(12345)
options(stringsAsFactors=FALSE)
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mydsutils.R")## Loading required package: caret
## Loading required package: lattice
## Loading required package: ggplot2source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
registerDoMC(4) # max(length(glb_txt_vars), glb_n_cv_folds) + 1
#packageVersion("tm")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")
# Analysis control global variables
glb_trnng_url <- "https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTrain.csv"
glb_newdt_url <- "https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTest.csv"
glb_out_pfx <- "dataclns_"
glb_save_envir <- FALSE # or TRUE
glb_is_separate_newobs_dataset <- TRUE # or TRUE
glb_split_entity_newobs_datasets <- TRUE # or FALSE
glb_split_newdata_method <- "sample" # "condition" or "sample" or "copy"
glb_split_newdata_condition <- NULL # or "is.na(<var>)"; "<var> <condition_operator> <value>"
glb_split_newdata_size_ratio <- 0.3 # > 0 & < 1
glb_split_sample.seed <- 123 # or any integer
glb_max_fitobs <- NULL # or any integer
glb_is_regression <- FALSE; glb_is_classification <- !glb_is_regression;
glb_is_binomial <- TRUE #or FALSE
glb_rsp_var_raw <- "sold"
# for classification, the response variable has to be a factor
glb_rsp_var <- "sold.fctr"
# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"),
# or contains spaces (e.g. "Not in Labor Force")
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- function(raw) {
# return(log(raw))
ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == 1, "Y", "N"); return(relevel(as.factor(ret_vals), ref="N"))
# #as.factor(paste0("B", raw))
# #as.factor(gsub(" ", "\\.", raw))
}
glb_map_rsp_raw_to_var(c(1, 1, 0, 0, NA))## [1] Y Y N N <NA>
## Levels: N Yglb_map_rsp_var_to_raw <- function(var) {
# return(exp(var))
as.numeric(var) - 1
# #as.numeric(var)
# #gsub("\\.", " ", levels(var)[as.numeric(var)])
# c("<=50K", " >50K")[as.numeric(var)]
# #c(FALSE, TRUE)[as.numeric(var)]
}
glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(c(1, 1, 0, 0, NA)))## [1] 1 1 0 0 NAif ((glb_rsp_var != glb_rsp_var_raw) & is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
glb_rsp_var_out <- paste0(glb_rsp_var, ".predict.") # model_id is appended later
# List info gathered for various columns
# <col_name>: <description>; <notes>
# description = The text description of the product provided by the seller.
# biddable = Whether this is an auction (biddable=1) or a sale with a fixed price (biddable=0).
# startprice = The start price (in US Dollars) for the auction (if biddable=1) or the sale price (if biddable=0).
# condition = The condition of the product (new, used, etc.)
# cellular = Whether the iPad has cellular connectivity (cellular=1) or not (cellular=0).
# carrier = The cellular carrier for which the iPad is equipped (if cellular=1); listed as "None" if cellular=0.
# color = The color of the iPad.
# storage = The iPad's storage capacity (in gigabytes).
# productline = The name of the product being sold.
# If multiple vars are parts of id, consider concatenating them to create one id var
# If glb_id_var == NULL, ".rownames <- row.names()" is the default
glb_id_var <- c("UniqueID")
glb_category_vars <- c("prdline.my")
glb_drop_vars <- c(NULL) # or c("<col_name>")
glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"
glb_assign_pairs_lst <- NULL;
# glb_assign_pairs_lst[["<var1>"]] <- list(from=c(NA),
# to=c("NA.my"))
glb_assign_vars <- names(glb_assign_pairs_lst)
# Derived features
glb_derive_lst <- NULL;
# Add logs of numerics that are not distributed normally -> do automatically ???
glb_derive_lst[["prdline.my"]] <- list(
mapfn=function(productline) { return(productline) }
, args=c("productline"))
glb_derive_lst[["startprice.log"]] <- list(
mapfn=function(startprice) { return(log(startprice)) }
, args=c("startprice"))
# glb_derive_lst[["startprice.log.zval"]] <- list(
# mapfn=function(startprice) { return(scale(log(startprice))) }
# , args=c("startprice"))
# mapfn=function(Rasmussen) { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) }
# mapfn=function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
# mapfn=function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
# mapfn=function(Week) { return(substr(Week, 1, 10)) }
# mapfn=function(raw) { tfr_raw <- as.character(cut(raw, 5));
# tfr_raw[is.na(tfr_raw)] <- "NA.my";
# return(as.factor(tfr_raw)) }
# , args=c("raw"))
# mapfn=function(PTS, oppPTS) { return(PTS - oppPTS) }
# , args=c("PTS", "oppPTS"))
# # If glb_allobs_df is not sorted in the desired manner
# mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glb_allobs_df)$ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }
# glb_derive_lst[["<txt_var>.niso8859.log"]] <- list(
# mapfn=function(<txt_var>) { match_lst <- gregexpr("&#[[:digit:]]{3};", <txt_var>)
# match_num_vctr <- unlist(lapply(match_lst,
# function(elem) length(elem)))
# return(log(1 + match_num_vctr)) }
# , args=c("<txt_var>"))
# mapfn=function(raw) { mod_raw <- raw;
# mod_raw <- gsub("&#[[:digit:]]{3};", " ", mod_raw);
# # Modifications for this exercise only
# mod_raw <- gsub("\\bgoodIn ", "good In", mod_raw);
# return(mod_raw)
# # Create user-specified pattern vectors
# #sum(mycount_pattern_occ("Metropolitan Diary:", glb_allobs_df$Abstract) > 0)
# if (txt_var %in% c("Snippet", "Abstract")) {
# txt_X_df[, paste0(txt_var_pfx, ".P.metropolitan.diary.colon")] <-
# as.integer(0 + mycount_pattern_occ("Metropolitan Diary:",
# glb_allobs_df[, txt_var]))
#summary(glb_allobs_df[ ,grep("P.on.this.day", names(glb_allobs_df), value=TRUE)])
# glb_derive_lst[["<var1>"]] <- glb_derive_lst[["<var2>"]]
glb_derive_vars <- names(glb_derive_lst)
# tst <- "PropR.fctr"; args_lst <- NULL; for (arg in glb_derive_lst[[tst]]$args) args_lst[[arg]] <- glb_allobs_df[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glb_derive_lst[[tst]]$mapfn, args_lst)));
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]);
glb_date_vars <- NULL # or c("<date_var>")
glb_date_fmts <- list(); #glb_date_fmts[["<date_var>"]] <- "%m/%e/%y"
glb_date_tzs <- list(); #glb_date_tzs[["<date_var>"]] <- "America/New_York"
#grep("America/New", OlsonNames(), value=TRUE)
glb_txt_vars <- c("description")
#Sys.setlocale("LC_ALL", "C") # For english
glb_txt_munge_filenames_pfx <- "mytxt_"
glb_append_stop_words <- list()
# Remember to use unstemmed words
#orderBy(~ -cor.y.abs, subset(glb_feats_df, grepl("[HSA]\\.T\\.", id) & !is.na(cor.high.X)))
#dsp_obs(Headline.contains="polit")
#subset(glb_allobs_df, H.T.compani > 0)[, c("UniqueID", "Headline", "H.T.compani")]
# glb_append_stop_words[["<txt_var1>"]] <- c(NULL
# # ,"<word1>" # <reason1>
# )
#subset(glb_allobs_df, S.T.newyorktim > 0)[, c("UniqueID", "Snippet", "S.T.newyorktim")]
#glb_txt_lst[["Snippet"]][which(glb_allobs_df$UniqueID %in% c(8394, 8317, 8339, 8350, 8307))]
glb_important_terms <- list()
# Remember to use stemmed terms
glb_sprs_thresholds <- c(0.950) # Generates ??? terms
# Properties:
# numrows(glb_feats_df) << numrows(glb_fitobs_df)
# Select terms that appear in at least 0.2 * O(FP/FN(glb_OOBobs_df))
# numrows(glb_OOBobs_df) = 1.1 * numrows(glb_newobs_df)
names(glb_sprs_thresholds) <- glb_txt_vars
# User-specified exclusions
glb_exclude_vars_as_features <- c("productline", "startprice")
if (glb_rsp_var_raw != glb_rsp_var)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_rsp_var_raw)
# List feats that shd be excluded due to known causation by prediction variable
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c(NULL)) # or c("<col_name>")
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer
glb_cluster <- FALSE # or TRUE
glb_interaction_only_features <- NULL # or ???
glb_models_lst <- list(); glb_models_df <- data.frame()
# Regression
if (glb_is_regression)
glb_models_method_vctr <- c("lm", "glm", "bayesglm", "rpart", "rf") else
# Classification
if (glb_is_binomial)
glb_models_method_vctr <- c("glm", "bayesglm", "rpart", "rf") else
glb_models_method_vctr <- c("rpart", "rf")
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<col_name>")
glb_model_metric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glb_model_metric <- NULL # or "<metric_name>"
glb_model_metric_maximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glb_model_metric_smmry <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glb_model_metric_terms)
# metric <- sum(confusion_mtrx * glb_model_metric_terms) / nrow(data)
# names(metric) <- glb_model_metric
# return(metric)
# }
glb_tune_models_df <-
rbind(
#data.frame(parameter="cp", min=0.00005, max=0.00005, by=0.000005),
#seq(from=0.01, to=0.01, by=0.01)
#data.frame(parameter="mtry", min=080, max=100, by=10),
#data.frame(parameter="mtry", min=08, max=10, by=1),
data.frame(parameter="dummy", min=2, max=4, by=1)
)
# or NULL
glb_n_cv_folds <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
glb_model_evl_criteria <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glb_model_evl_criteria <-
c("max.Accuracy.OOB", "max.auc.OOB", "max.Kappa.OOB", "min.aic.fit") else
glb_model_evl_criteria <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}
glb_sel_mdl_id <- NULL #"Low.cor.X.glm"
glb_fin_mdl_id <- glb_sel_mdl_id # or "Final"
# Depict process
glb_analytics_pn <- petrinet(name="glb_analytics_pn",
trans_df=data.frame(id=1:6,
name=c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df=data.frame(
begin=c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end =c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_chunks_df <- myadd_chunk(NULL, "import.data")## label step_major step_minor bgn end elapsed
## 1 import.data 1 0 9.733 NA NAStep 1.0: import data
chunk option: eval=
#glb_chunks_df <- myadd_chunk(NULL, "import.data")
glb_trnobs_df <- myimport_data(url=glb_trnng_url, comment="glb_trnobs_df",
force_header=TRUE)## [1] "Reading file ./data/eBayiPadTrain.csv..."
## [1] "dimensions of data in ./data/eBayiPadTrain.csv: 1,861 rows x 11 cols"
## description
## 1 iPad is in 8.5+ out of 10 cosmetic condition!
## 2 Previously used, please read description. May show signs of use such as scratches to the screen and
## 3
## 4
## 5 Please feel free to buy. All products have been thoroughly inspected, cleaned and tested to be 100%
## 6
## biddable startprice condition cellular carrier color
## 1 0 159.99 Used 0 None Black
## 2 1 0.99 Used 1 Verizon Unknown
## 3 0 199.99 Used 0 None White
## 4 0 235.00 New other (see details) 0 None Unknown
## 5 0 199.99 Seller refurbished Unknown Unknown Unknown
## 6 1 175.00 Used 1 AT&T Space Gray
## storage productline sold UniqueID
## 1 16 iPad 2 0 10001
## 2 16 iPad 2 1 10002
## 3 16 iPad 4 1 10003
## 4 16 iPad mini 2 0 10004
## 5 Unknown Unknown 0 10005
## 6 32 iPad mini 2 1 10006
## description
## 65
## 283 Pristine condition, comes with a case and stylus.
## 948 \x89\xdb\xcfUsed Apple Ipad 16 gig 1st generation in Great working condition and 100% functional.Very little
## 1354
## 1366 Item still in complete working order, minor scratches, normal wear and tear but no damage. screen is
## 1840
## biddable startprice condition cellular carrier color
## 65 0 195.00 Used 0 None Unknown
## 283 1 20.00 Used 0 None Unknown
## 948 0 110.00 Seller refurbished 0 None Black
## 1354 0 300.00 Used 0 None White
## 1366 1 125.00 Used Unknown Unknown Unknown
## 1840 0 249.99 Used 1 Sprint Space Gray
## storage productline sold UniqueID
## 65 16 iPad mini 0 10065
## 283 64 iPad 1 0 10283
## 948 32 iPad 1 0 10948
## 1354 16 iPad Air 1 11354
## 1366 Unknown iPad 1 1 11366
## 1840 16 iPad Air 1 11840
## description
## 1856 Overall item is in good condition and is fully operational and ready to use. Comes with box and
## 1857 Used. Tested. Guaranteed to work. Physical condition grade B+ does have some light scratches and
## 1858 This item is brand new and was never used; however, the box and/or packaging has been opened.
## 1859
## 1860 This unit has minor scratches on case and several small scratches on the display. \nIt is in
## 1861 30 Day Warranty. Fully functional engraved iPad 1st Generation with signs of normal wear which
## biddable startprice condition cellular carrier
## 1856 0 89.50 Used 1 AT&T
## 1857 0 239.95 Used 0 None
## 1858 0 329.99 New other (see details) 0 None
## 1859 0 400.00 New 0 None
## 1860 0 89.00 Seller refurbished 0 None
## 1861 0 119.99 Used 1 AT&T
## color storage productline sold UniqueID
## 1856 Unknown 16 iPad 1 0 11856
## 1857 Black 32 iPad 4 1 11857
## 1858 Space Gray 16 iPad Air 0 11858
## 1859 Gold 16 iPad mini 3 0 11859
## 1860 Black 64 iPad 1 1 11860
## 1861 Black 64 iPad 1 0 11861
## 'data.frame': 1861 obs. of 11 variables:
## $ description: chr "iPad is in 8.5+ out of 10 cosmetic condition!" "Previously used, please read description. May show signs of use such as scratches to the screen and " "" "" ...
## $ biddable : int 0 1 0 0 0 1 1 0 1 1 ...
## $ startprice : num 159.99 0.99 199.99 235 199.99 ...
## $ condition : chr "Used" "Used" "Used" "New other (see details)" ...
## $ cellular : chr "0" "1" "0" "0" ...
## $ carrier : chr "None" "Verizon" "None" "None" ...
## $ color : chr "Black" "Unknown" "White" "Unknown" ...
## $ storage : chr "16" "16" "16" "16" ...
## $ productline: chr "iPad 2" "iPad 2" "iPad 4" "iPad mini 2" ...
## $ sold : int 0 1 1 0 0 1 1 0 1 1 ...
## $ UniqueID : int 10001 10002 10003 10004 10005 10006 10007 10008 10009 10010 ...
## - attr(*, "comment")= chr "glb_trnobs_df"
## NULL# glb_trnobs_df <- read.delim("data/hygiene.txt", header=TRUE, fill=TRUE, sep="\t",
# fileEncoding='iso-8859-1')
# glb_trnobs_df <- read.table("data/hygiene.dat.labels", col.names=c("dirty"),
# na.strings="[none]")
# glb_trnobs_df$review <- readLines("data/hygiene.dat", n =-1)
# comment(glb_trnobs_df) <- "glb_trnobs_df"
# glb_trnobs_df <- data.frame()
# for (symbol in c("Boeing", "CocaCola", "GE", "IBM", "ProcterGamble")) {
# sym_trnobs_df <-
# myimport_data(url=gsub("IBM", symbol, glb_trnng_url), comment="glb_trnobs_df",
# force_header=TRUE)
# sym_trnobs_df$Symbol <- symbol
# glb_trnobs_df <- myrbind_df(glb_trnobs_df, sym_trnobs_df)
# }
# glb_trnobs_df <-
# glb_trnobs_df %>% dplyr::filter(Year >= 1999)
if (glb_is_separate_newobs_dataset) {
glb_newobs_df <- myimport_data(url=glb_newdt_url, comment="glb_newobs_df",
force_header=TRUE)
# To make plots / stats / checks easier in chunk:inspectORexplore.data
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df);
comment(glb_allobs_df) <- "glb_allobs_df"
} else {
glb_allobs_df <- glb_trnobs_df; comment(glb_allobs_df) <- "glb_allobs_df"
if (!glb_split_entity_newobs_datasets) {
stop("Not implemented yet")
glb_newobs_df <- glb_trnobs_df[sample(1:nrow(glb_trnobs_df),
max(2, nrow(glb_trnobs_df) / 1000)),]
} else if (glb_split_newdata_method == "condition") {
glb_newobs_df <- do.call("subset",
list(glb_trnobs_df, parse(text=glb_split_newdata_condition)))
glb_trnobs_df <- do.call("subset",
list(glb_trnobs_df, parse(text=paste0("!(",
glb_split_newdata_condition,
")"))))
} else if (glb_split_newdata_method == "sample") {
require(caTools)
set.seed(glb_split_sample.seed)
split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw],
SplitRatio=(1-glb_split_newdata_size_ratio))
glb_newobs_df <- glb_trnobs_df[!split, ]
glb_trnobs_df <- glb_trnobs_df[split ,]
} else if (glb_split_newdata_method == "copy") {
glb_trnobs_df <- glb_allobs_df
comment(glb_trnobs_df) <- "glb_trnobs_df"
glb_newobs_df <- glb_allobs_df
comment(glb_newobs_df) <- "glb_newobs_df"
} else stop("glb_split_newdata_method should be %in% c('condition', 'sample', 'copy')")
comment(glb_newobs_df) <- "glb_newobs_df"
myprint_df(glb_newobs_df)
str(glb_newobs_df)
if (glb_split_entity_newobs_datasets) {
myprint_df(glb_trnobs_df)
str(glb_trnobs_df)
}
} ## [1] "Reading file ./data/eBayiPadTest.csv..."
## [1] "dimensions of data in ./data/eBayiPadTest.csv: 798 rows x 10 cols"
## description
## 1 like new
## 2 Item is in great shape. I upgraded to the iPad Air 2 and don't need the mini any longer, even though
## 3 This iPad is working and is tested 100%. It runs great. It is in good condition. Cracked digitizer.
## 4
## 5 Grade A condition means that the Ipad is 100% working condition. Cosmetically 8/9 out of 10 - Will
## 6 Brand new factory sealed iPad in an OPEN BOX...THE BOX ITSELF IS HEAVILY DISTRESSED(see
## biddable startprice condition cellular carrier color
## 1 0 105.00 Used 1 AT&T Unknown
## 2 0 195.00 Used 0 None Unknown
## 3 0 219.99 Used 0 None Unknown
## 4 1 100.00 Used 0 None Unknown
## 5 0 210.99 Manufacturer refurbished 0 None Black
## 6 0 514.95 New other (see details) 0 None Gold
## storage productline UniqueID
## 1 32 iPad 1 11862
## 2 16 iPad mini 2 11863
## 3 64 iPad 3 11864
## 4 16 iPad mini 11865
## 5 32 iPad 3 11866
## 6 64 iPad Air 2 11867
## description
## 1 like new
## 142 iPad mini 1st gen wi-fi 16gb is in perfect working order.
## 309 In excellent condition. Minor scratches on the back. Screen in mint condition. Comes in original
## 312 iPad is in Great condition, the screen is in great condition showing only a few minor scratches, the
## 320 Good condition and fully functional
## 369
## biddable startprice condition cellular carrier color storage
## 1 0 105.00 Used 1 AT&T Unknown 32
## 142 1 0.99 Used 0 None Unknown 16
## 309 0 200.00 Used 1 AT&T Black 32
## 312 1 0.99 Used 0 None Unknown 16
## 320 1 60.00 Used 0 None White 16
## 369 1 197.97 Used 0 None Unknown 64
## productline UniqueID
## 1 iPad 1 11862
## 142 iPad mini 12003
## 309 iPad 3 12170
## 312 iPad mini 2 12173
## 320 iPad 1 12181
## 369 iPad mini 3 12230
## description
## 793 Crack on digitizer near top. Top line of digitizer does not respond to touch. Other than that, all
## 794
## 795
## 796
## 797
## 798 Slightly Used. Includes everything you need plus a nice leather case!\nThere is a slice mark on the
## biddable startprice condition cellular carrier color
## 793 0 104.00 For parts or not working 1 Unknown Black
## 794 0 95.00 Used 1 AT&T Unknown
## 795 1 199.99 Manufacturer refurbished 0 None White
## 796 0 149.99 Used 0 None Unknown
## 797 0 7.99 New Unknown Unknown Unknown
## 798 0 139.00 Used 1 Unknown Black
## storage productline UniqueID
## 793 16 iPad 2 12654
## 794 64 iPad 1 12655
## 795 16 iPad 4 12656
## 796 16 iPad 2 12657
## 797 Unknown iPad 3 12658
## 798 32 Unknown 12659
## 'data.frame': 798 obs. of 10 variables:
## $ description: chr "like new" "Item is in great shape. I upgraded to the iPad Air 2 and don't need the mini any longer, even though " "This iPad is working and is tested 100%. It runs great. It is in good condition. Cracked digitizer." "" ...
## $ biddable : int 0 0 0 1 0 0 0 0 0 1 ...
## $ startprice : num 105 195 220 100 211 ...
## $ condition : chr "Used" "Used" "Used" "Used" ...
## $ cellular : chr "1" "0" "0" "0" ...
## $ carrier : chr "AT&T" "None" "None" "None" ...
## $ color : chr "Unknown" "Unknown" "Unknown" "Unknown" ...
## $ storage : chr "32" "16" "64" "16" ...
## $ productline: chr "iPad 1" "iPad mini 2" "iPad 3" "iPad mini" ...
## $ UniqueID : int 11862 11863 11864 11865 11866 11867 11868 11869 11870 11871 ...
## - attr(*, "comment")= chr "glb_newobs_df"
## NULLif ((num_nas <- sum(is.na(glb_trnobs_df[, glb_rsp_var_raw]))) > 0)
stop("glb_trnobs_df$", glb_rsp_var_raw, " contains NAs for ", num_nas, " obs")
if (nrow(glb_trnobs_df) == nrow(glb_allobs_df))
warning("glb_trnobs_df same as glb_allobs_df")
if (nrow(glb_newobs_df) == nrow(glb_allobs_df))
warning("glb_newobs_df same as glb_allobs_df")
if (length(glb_drop_vars) > 0) {
warning("dropping vars: ", paste0(glb_drop_vars, collapse=", "))
glb_allobs_df <- glb_allobs_df[, setdiff(names(glb_allobs_df), glb_drop_vars)]
glb_trnobs_df <- glb_trnobs_df[, setdiff(names(glb_trnobs_df), glb_drop_vars)]
glb_newobs_df <- glb_newobs_df[, setdiff(names(glb_newobs_df), glb_drop_vars)]
}
#stop(here"); sav_allobs_df <- glb_allobs_df # glb_allobs_df <- sav_allobs_df
# Combine trnent & newobs into glb_allobs_df for easier manipulation
glb_trnobs_df$.src <- "Train"; glb_newobs_df$.src <- "Test";
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, ".src")
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df)
comment(glb_allobs_df) <- "glb_allobs_df"
# Check for duplicates in glb_id_var
if (length(glb_id_var) == 0) {
warning("using .rownames as identifiers for observations")
glb_allobs_df$.rownames <- rownames(glb_allobs_df)
glb_trnobs_df$.rownames <- rownames(subset(glb_allobs_df, .src == "Train"))
glb_newobs_df$.rownames <- rownames(subset(glb_allobs_df, .src == "Test"))
glb_id_var <- ".rownames"
}
if (sum(duplicated(glb_allobs_df[, glb_id_var, FALSE])) > 0)
stop(glb_id_var, " duplicated in glb_allobs_df")
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_id_var)
glb_allobs_df <- orderBy(reformulate(glb_id_var), glb_allobs_df)
glb_trnobs_df <- glb_newobs_df <- NULL
glb_chunks_df <- myadd_chunk(glb_chunks_df, "inspect.data", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 1 import.data 1 0 9.733 10.197 0.464
## 2 inspect.data 2 0 10.197 NA NAStep 2.0: inspect data
#print(str(glb_allobs_df))
#View(glb_allobs_df)
dsp_class_dstrb <- function(var) {
xtab_df <- mycreate_xtab_df(glb_allobs_df, c(".src", var))
rownames(xtab_df) <- xtab_df$.src
xtab_df <- subset(xtab_df, select=-.src)
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
# Performed repeatedly in other chunks
glb_chk_data <- function() {
# Histogram of predictor in glb_trnobs_df & glb_newobs_df
print(myplot_histogram(glb_allobs_df, glb_rsp_var_raw) + facet_wrap(~ .src))
if (glb_is_classification)
dsp_class_dstrb(var=ifelse(glb_rsp_var %in% names(glb_allobs_df),
glb_rsp_var, glb_rsp_var_raw))
mycheck_problem_data(glb_allobs_df)
}
glb_chk_data()## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## Loading required package: reshape2
## sold.0 sold.1 sold.NA
## Test NA NA 798
## Train 1001 860 NA
## sold.0 sold.1 sold.NA
## Test NA NA 1
## Train 0.5378829 0.4621171 NA
## [1] "numeric data missing in glb_allobs_df: "
## sold
## 798
## [1] "numeric data w/ 0s in glb_allobs_df: "
## biddable sold
## 1446 1001
## [1] "numeric data w/ Infs in glb_allobs_df: "
## named integer(0)
## [1] "numeric data w/ NaNs in glb_allobs_df: "
## named integer(0)
## [1] "string data missing in glb_allobs_df: "
## description condition cellular carrier color storage
## 1521 0 0 0 0 0
## productline
## 0# Create new features that help diagnostics
if (!is.null(glb_map_rsp_raw_to_var)) {
glb_allobs_df[, glb_rsp_var] <-
glb_map_rsp_raw_to_var(glb_allobs_df[, glb_rsp_var_raw])
mycheck_map_results(mapd_df=glb_allobs_df,
from_col_name=glb_rsp_var_raw, to_col_name=glb_rsp_var)
if (glb_is_classification) dsp_class_dstrb(glb_rsp_var)
}## Loading required package: sqldf
## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: RSQLite
## Loading required package: DBI
## Loading required package: tcltk## sold sold.fctr .n
## 1 0 N 1001
## 2 1 Y 860
## 3 NA <NA> 798## Warning: Removed 1 rows containing missing values (position_stack).
## sold.fctr.N sold.fctr.Y sold.fctr.NA
## Test NA NA 798
## Train 1001 860 NA
## sold.fctr.N sold.fctr.Y sold.fctr.NA
## Test NA NA 1
## Train 0.5378829 0.4621171 NA# check distribution of all numeric data
dsp_numeric_feats_dstrb <- function(feats_vctr) {
for (feat in feats_vctr) {
print(sprintf("feat: %s", feat))
if (glb_is_regression)
gp <- myplot_scatter(df=glb_allobs_df, ycol_name=glb_rsp_var, xcol_name=feat,
smooth=TRUE)
if (glb_is_classification)
gp <- myplot_box(df=glb_allobs_df, ycol_names=feat, xcol_name=glb_rsp_var)
if (inherits(glb_allobs_df[, feat], "factor"))
gp <- gp + facet_wrap(reformulate(feat))
print(gp)
}
}
# dsp_numeric_vars_dstrb(setdiff(names(glb_allobs_df),
# union(myfind_chr_cols_df(glb_allobs_df),
# c(glb_rsp_var_raw, glb_rsp_var))))
add_new_diag_feats <- function(obs_df, ref_df=glb_allobs_df) {
require(plyr)
obs_df <- mutate(obs_df,
# <col_name>.NA=is.na(<col_name>),
# <col_name>.fctr=factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# <col_name>.fctr=relevel(factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# "<ref_val>"),
# <col2_name>.fctr=relevel(factor(ifelse(<col1_name> == <val>, "<oth_val>", "<ref_val>")),
# as.factor(c("R", "<ref_val>")),
# ref="<ref_val>"),
# This doesn't work - use sapply instead
# <col_name>.fctr_num=grep(<col_name>, levels(<col_name>.fctr)),
#
# Date.my=as.Date(strptime(Date, "%m/%d/%y %H:%M")),
# Year=year(Date.my),
# Month=months(Date.my),
# Weekday=weekdays(Date.my)
# <col_name>=<table>[as.character(<col2_name>)],
# <col_name>=as.numeric(<col2_name>),
# <col_name> = trunc(<col2_name> / 100),
.rnorm = rnorm(n=nrow(obs_df))
)
# If levels of a factor are different across obs_df & glb_newobs_df; predict.glm fails
# Transformations not handled by mutate
# obs_df$<col_name>.fctr.num <- sapply(1:nrow(obs_df),
# function(row_ix) grep(obs_df[row_ix, "<col_name>"],
# levels(obs_df[row_ix, "<col_name>.fctr"])))
#print(summary(obs_df))
#print(sapply(names(obs_df), function(col) sum(is.na(obs_df[, col]))))
return(obs_df)
}
glb_allobs_df <- add_new_diag_feats(glb_allobs_df)## Loading required package: plyrrequire(dplyr)## Loading required package: dplyr
##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
##
## The following objects are masked from 'package:stats':
##
## filter, lag
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union#stop(here"); sav_allobs_df <- glb_allobs_df # glb_allobs_df <- sav_allobs_df
# Merge some <descriptor>
# glb_allobs_df$<descriptor>.my <- glb_allobs_df$<descriptor>
# glb_allobs_df[grepl("\\bAIRPORT\\b", glb_allobs_df$<descriptor>.my),
# "<descriptor>.my"] <- "AIRPORT"
# glb_allobs_df$<descriptor>.my <-
# plyr::revalue(glb_allobs_df$<descriptor>.my, c(
# "ABANDONED BUILDING" = "OTHER",
# "##" = "##"
# ))
# print(<descriptor>_freq_df <- mycreate_sqlxtab_df(glb_allobs_df, c("<descriptor>.my")))
# # print(dplyr::filter(<descriptor>_freq_df, grepl("(MEDICAL|DENTAL|OFFICE)", <descriptor>.my)))
# # print(dplyr::filter(dplyr::select(glb_allobs_df, -<var.zoo>),
# # grepl("STORE", <descriptor>.my)))
# glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features, "<descriptor>")
# Check distributions of newly transformed / extracted vars
# Enhancement: remove vars that were displayed ealier
dsp_numeric_feats_dstrb(feats_vctr=setdiff(names(glb_allobs_df),
c(myfind_chr_cols_df(glb_allobs_df), glb_rsp_var_raw, glb_rsp_var,
glb_exclude_vars_as_features)))## [1] "feat: biddable"
## [1] "feat: .rnorm"
# Convert factors to dummy variables
# Build splines require(splines); bsBasis <- bs(training$age, df=3)
#pairs(subset(glb_trnobs_df, select=-c(col_symbol)))
# Check for glb_newobs_df & glb_trnobs_df features range mismatches
# Other diagnostics:
# print(subset(glb_trnobs_df, <col1_name> == max(glb_trnobs_df$<col1_name>, na.rm=TRUE) &
# <col2_name> <= mean(glb_trnobs_df$<col1_name>, na.rm=TRUE)))
# print(glb_trnobs_df[which.max(glb_trnobs_df$<col_name>),])
# print(<col_name>_freq_glb_trnobs_df <- mycreate_tbl_df(glb_trnobs_df, "<col_name>"))
# print(which.min(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>)[, 2]))
# print(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>))
# print(table(is.na(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(table(sign(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(mycreate_xtab_df(glb_trnobs_df, <col1_name>))
# print(mycreate_xtab_df(glb_trnobs_df, c(<col1_name>, <col2_name>)))
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <-
# mycreate_xtab_df(glb_trnobs_df, c("<col1_name>", "<col2_name>")))
# <col1_name>_<col2_name>_xtab_glb_trnobs_df[is.na(<col1_name>_<col2_name>_xtab_glb_trnobs_df)] <- 0
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <-
# mutate(<col1_name>_<col2_name>_xtab_glb_trnobs_df,
# <col3_name>=(<col1_name> * 1.0) / (<col1_name> + <col2_name>)))
# print(mycreate_sqlxtab_df(glb_allobs_df, c("<col1_name>", "<col2_name>")))
# print(<col2_name>_min_entity_arr <-
# sort(tapply(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>, min, na.rm=TRUE)))
# print(<col1_name>_na_by_<col2_name>_arr <-
# sort(tapply(glb_trnobs_df$<col1_name>.NA, glb_trnobs_df$<col2_name>, mean, na.rm=TRUE)))
# Other plots:
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>"))
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>", xcol_name="<col2_name>"))
# print(myplot_line(subset(glb_trnobs_df, Symbol %in% c("CocaCola", "ProcterGamble")),
# "Date.POSIX", "StockPrice", facet_row_colnames="Symbol") +
# geom_vline(xintercept=as.numeric(as.POSIXlt("2003-03-01"))) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1983-01-01")))
# )
# print(myplot_line(subset(glb_trnobs_df, Date.POSIX > as.POSIXct("2004-01-01")),
# "Date.POSIX", "StockPrice") +
# geom_line(aes(color=Symbol)) +
# coord_cartesian(xlim=c(as.POSIXct("1990-01-01"),
# as.POSIXct("2000-01-01"))) +
# coord_cartesian(ylim=c(0, 250)) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1997-09-01"))) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1997-11-01")))
# )
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", colorcol_name="<Pred.fctr>") +
# geom_point(data=subset(glb_allobs_df, <condition>),
# mapping=aes(x=<x_var>, y=<y_var>), color="red", shape=4, size=5) +
# geom_vline(xintercept=84))
glb_chunks_df <- myadd_chunk(glb_chunks_df, "scrub.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 2 inspect.data 2 0 10.197 16.802 6.605
## 3 scrub.data 2 1 16.802 NA NAStep 2.1: scrub data
mycheck_problem_data(glb_allobs_df)## [1] "numeric data missing in glb_allobs_df: "
## sold sold.fctr
## 798 798
## [1] "numeric data w/ 0s in glb_allobs_df: "
## biddable sold
## 1446 1001
## [1] "numeric data w/ Infs in glb_allobs_df: "
## named integer(0)
## [1] "numeric data w/ NaNs in glb_allobs_df: "
## named integer(0)
## [1] "string data missing in glb_allobs_df: "
## description condition cellular carrier color storage
## 1521 0 0 0 0 0
## productline
## 0findOffendingCharacter <- function(x, maxStringLength=256){
print(x)
for (c in 1:maxStringLength){
offendingChar <- substr(x,c,c)
#print(offendingChar) #uncomment if you want the indiv characters printed
#the next character is the offending multibyte Character
}
}
# string_vector <- c("test", "Se\x96ora", "works fine")
# lapply(string_vector, findOffendingCharacter)
# lapply(glb_allobs_df$description[29], findOffendingCharacter)
sel_obs <- function(vars_lst, ignore.case=TRUE, perl=FALSE) {
tmp_df <- glb_allobs_df
# Does not work for Popular == NAs ???
# if (!is.null(Popular)) {
# if (is.na(Popular))
# tmp_df <- tmp_df[is.na(tmp_df$Popular), ] else
# tmp_df <- tmp_df[tmp_df$Popular == Popular, ]
# }
# if (!is.null(NewsDesk))
# tmp_df <- tmp_df[tmp_df$NewsDesk == NewsDesk, ]
for (var in names(vars_lst)) {
if (grepl(".contains", var))
tmp_df <- tmp_df[grep(vars_lst[var],
tmp_df[, unlist(strsplit(var, ".contains"))],
ignore.case=ignore.case, perl=perl), ]
else
tmp_df <- tmp_df[tmp_df[, var] == vars_lst[var], ]
}
return(glb_allobs_df[, glb_id_var] %in% tmp_df[, glb_id_var])
}
#print(glb_allobs_df[sel_obs(list(description.contains="mini(?!m)"), perl=TRUE), "description"])
dsp_obs <- function(..., cols=c(NULL), all=FALSE) {
tmp_df <- glb_allobs_df[sel_obs(...),
c(glb_id_var, glb_rsp_var, glb_category_vars, glb_txt_vars, cols),
FALSE]
if(all) { print(tmp_df) } else { myprint_df(tmp_df) }
}
#dsp_obs(list(description.contains="mini(?!m)"), perl=TRUE)
#dsp_obs(Popular=1, NewsDesk="", SectionName="", Headline.contains="Boehner")
# dsp_obs(Popular=1, NewsDesk="", SectionName="")
# dsp_obs(Popular=NA, NewsDesk="", SectionName="")
dsp_hdlxtab <- function(str)
print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "Headline", glb_rsp_var)))
#dsp_hdlxtab("(1914)|(1939)")
dsp_catxtab <- function(str)
print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# dsp_catxtab("1914)|(1939)")
# dsp_catxtab("19(14|39|64):")
# dsp_catxtab("19..:")
# Merge some categories
# glb_allobs_df$myCategory <-
# plyr::revalue(glb_allobs_df$myCategory, c(
# "#Business Day#Dealbook" = "Business#Business Day#Dealbook",
# "#Business Day#Small Business" = "Business#Business Day#Small Business",
# "dummy" = "dummy"
# ))
# ctgry_xtab_df <- orderBy(reformulate(c("-", ".n")),
# mycreate_sqlxtab_df(glb_allobs_df,
# c("myCategory", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# myprint_df(ctgry_xtab_df)
# write.table(ctgry_xtab_df, paste0(glb_out_pfx, "ctgry_xtab.csv"),
# row.names=FALSE)
# ctgry_cast_df <- orderBy(~ -Y -NA, dcast(ctgry_xtab_df,
# myCategory + NewsDesk + SectionName + SubsectionName ~
# Popular.fctr, sum, value.var=".n"))
# myprint_df(ctgry_cast_df)
# write.table(ctgry_cast_df, paste0(glb_out_pfx, "ctgry_cast.csv"),
# row.names=FALSE)
# print(ctgry_sum_tbl <- table(glb_allobs_df$myCategory, glb_allobs_df[, glb_rsp_var],
# useNA="ifany"))
dsp_chisq.test <- function(...) {
sel_df <- glb_allobs_df[sel_obs(...) &
!is.na(glb_allobs_df$Popular), ]
sel_df$.marker <- 1
ref_df <- glb_allobs_df[!is.na(glb_allobs_df$Popular), ]
mrg_df <- merge(ref_df[, c(glb_id_var, "Popular")],
sel_df[, c(glb_id_var, ".marker")], all.x=TRUE)
mrg_df[is.na(mrg_df)] <- 0
print(mrg_tbl <- table(mrg_df$.marker, mrg_df$Popular))
print("Rows:Selected; Cols:Popular")
#print(mrg_tbl)
print(chisq.test(mrg_tbl))
}
# dsp_chisq.test(Headline.contains="[Ee]bola")
# dsp_chisq.test(Snippet.contains="[Ee]bola")
# dsp_chisq.test(Abstract.contains="[Ee]bola")
# print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains="[Ee]bola"), ],
# c(glb_rsp_var, "NewsDesk", "SectionName", "SubsectionName")))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName))
# print(table(glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
# glb_allobs_df$myCategory.fctr <- as.factor(glb_allobs_df$myCategory)
# glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
# c("myCategory", "NewsDesk", "SectionName", "SubsectionName"))
print(table(glb_allobs_df$cellular, glb_allobs_df$carrier, useNA="ifany"))##
## AT&T None Other Sprint T-Mobile Unknown Verizon
## 0 0 1595 0 0 0 0 0
## 1 288 0 4 36 27 172 196
## Unknown 4 4 2 0 0 331 0# glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
# (glb_allobs_df$carrier %in% c("AT&T", "Other")),
# c(glb_id_var, glb_rsp_var_raw, "description", "carrier", "cellular")]
glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
(glb_allobs_df$carrier %in% c("AT&T", "Other")),
"cellular"] <- "1"
# glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
# (glb_allobs_df$carrier %in% c("None")),
# c(glb_id_var, glb_rsp_var_raw, "description", "carrier", "cellular")]
glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
(glb_allobs_df$carrier %in% c("None")),
"cellular"] <- "0"
print(table(glb_allobs_df$cellular, glb_allobs_df$carrier, useNA="ifany"))##
## AT&T None Other Sprint T-Mobile Unknown Verizon
## 0 0 1599 0 0 0 0 0
## 1 292 0 6 36 27 172 196
## Unknown 0 0 0 0 0 331 0Step 2.1: scrub data
glb_chunks_df <- myadd_chunk(glb_chunks_df, "transform.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 3 scrub.data 2 1 16.802 17.632 0.83
## 4 transform.data 2 2 17.632 NA NA### Mapping dictionary
#sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
if (!is.null(glb_map_vars)) {
for (feat in glb_map_vars) {
map_df <- myimport_data(url=glb_map_urls[[feat]],
comment="map_df",
print_diagn=TRUE)
glb_allobs_df <- mymap_codes(glb_allobs_df, feat, names(map_df)[2],
map_df, map_join_col_name=names(map_df)[1],
map_tgt_col_name=names(map_df)[2])
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_map_vars)
}
### Forced Assignments
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (feat in glb_assign_vars) {
new_feat <- paste0(feat, ".my")
print(sprintf("Forced Assignments for: %s -> %s...", feat, new_feat))
glb_allobs_df[, new_feat] <- glb_allobs_df[, feat]
pairs <- glb_assign_pairs_lst[[feat]]
for (pair_ix in 1:length(pairs$from)) {
if (is.na(pairs$from[pair_ix]))
nobs <- nrow(filter(glb_allobs_df,
is.na(eval(parse(text=feat),
envir=glb_allobs_df)))) else
nobs <- sum(glb_allobs_df[, feat] == pairs$from[pair_ix])
#nobs <- nrow(filter(glb_allobs_df, is.na(Married.fctr))) ; print(nobs)
if ((is.na(pairs$from[pair_ix])) && (is.na(pairs$to[pair_ix])))
stop("what are you trying to do ???")
if (is.na(pairs$from[pair_ix]))
glb_allobs_df[is.na(glb_allobs_df[, feat]), new_feat] <-
pairs$to[pair_ix] else
glb_allobs_df[glb_allobs_df[, feat] == pairs$from[pair_ix], new_feat] <-
pairs$to[pair_ix]
print(sprintf(" %s -> %s for %s obs",
pairs$from[pair_ix], pairs$to[pair_ix], format(nobs, big.mark=",")))
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_assign_vars)
}
### Derivations using mapping functions
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (new_feat in glb_derive_vars) {
print(sprintf("Creating new feature: %s...", new_feat))
args_lst <- NULL
for (arg in glb_derive_lst[[new_feat]]$args)
args_lst[[arg]] <- glb_allobs_df[, arg]
glb_allobs_df[, new_feat] <- do.call(glb_derive_lst[[new_feat]]$mapfn, args_lst)
}## [1] "Creating new feature: prdline.my..."
## [1] "Creating new feature: startprice.log..."Step 2.2: transform data
#```{r extract_features, cache=FALSE, eval=!is.null(glb_txt_vars)}
glb_chunks_df <- myadd_chunk(glb_chunks_df, "extract.features", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 4 transform.data 2 2 17.632 17.722 0.09
## 5 extract.features 3 0 17.722 NA NAextract.features_chunk_df <- myadd_chunk(NULL, "extract.features_bgn")## label step_major step_minor bgn end elapsed
## 1 extract.features_bgn 1 0 17.729 NA NA# Options:
# Select Tf, log(1 + Tf), Tf-IDF or BM25Tf-IDf
# Create new features that help prediction
# <col_name>.lag.2 <- lag(zoo(glb_trnobs_df$<col_name>), -2, na.pad=TRUE)
# glb_trnobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
# <col_name>.lag.2 <- lag(zoo(glb_newobs_df$<col_name>), -2, na.pad=TRUE)
# glb_newobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
#
# glb_newobs_df[1, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df) - 1,
# "<col_name>"]
# glb_newobs_df[2, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df),
# "<col_name>"]
# glb_allobs_df <- mutate(glb_allobs_df,
# A.P.http=ifelse(grepl("http",Added,fixed=TRUE), 1, 0)
# )
#
# glb_trnobs_df <- mutate(glb_trnobs_df,
# )
#
# glb_newobs_df <- mutate(glb_newobs_df,
# )
# Convert dates to numbers
# typically, dates come in as chars;
# so this must be done before converting chars to factors
#stop(here"); sav_allobs_df <- glb_allobs_df #; glb_allobs_df <- sav_allobs_df
if (!is.null(glb_date_vars)) {
glb_allobs_df <- cbind(glb_allobs_df,
myextract_dates_df(df=glb_allobs_df, vars=glb_date_vars,
id_vars=glb_id_var, rsp_var=glb_rsp_var))
for (sfx in c("", ".POSIX"))
glb_exclude_vars_as_features <-
union(glb_exclude_vars_as_features,
paste(glb_date_vars, sfx, sep=""))
for (feat in glb_date_vars) {
glb_allobs_df <- orderBy(reformulate(paste0(feat, ".POSIX")), glb_allobs_df)
# print(myplot_scatter(glb_allobs_df, xcol_name=paste0(feat, ".POSIX"),
# ycol_name=glb_rsp_var, colorcol_name=glb_rsp_var))
print(myplot_scatter(glb_allobs_df[glb_allobs_df[, paste0(feat, ".POSIX")] >=
strptime("2012-12-01", "%Y-%m-%d"), ],
xcol_name=paste0(feat, ".POSIX"),
ycol_name=glb_rsp_var, colorcol_name=paste0(feat, ".wkend")))
# Create features that measure the gap between previous timestamp in the data
require(zoo)
z <- zoo(as.numeric(as.POSIXlt(glb_allobs_df[, paste0(feat, ".POSIX")])))
glb_allobs_df[, paste0(feat, ".zoo")] <- z
print(head(glb_allobs_df[, c(glb_id_var, feat, paste0(feat, ".zoo"))]))
print(myplot_scatter(glb_allobs_df[glb_allobs_df[, paste0(feat, ".POSIX")] >
strptime("2012-10-01", "%Y-%m-%d"), ],
xcol_name=paste0(feat, ".zoo"), ycol_name=glb_rsp_var,
colorcol_name=glb_rsp_var))
b <- zoo(, seq(nrow(glb_allobs_df)))
last1 <- as.numeric(merge(z-lag(z, -1), b, all=TRUE)); last1[is.na(last1)] <- 0
glb_allobs_df[, paste0(feat, ".last1.log")] <- log(1 + last1)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last1.log")] > 0, ],
ycol_names=paste0(feat, ".last1.log"),
xcol_name=glb_rsp_var))
last2 <- as.numeric(merge(z-lag(z, -2), b, all=TRUE)); last2[is.na(last2)] <- 0
glb_allobs_df[, paste0(feat, ".last2.log")] <- log(1 + last2)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last2.log")] > 0, ],
ycol_names=paste0(feat, ".last2.log"),
xcol_name=glb_rsp_var))
last10 <- as.numeric(merge(z-lag(z, -10), b, all=TRUE)); last10[is.na(last10)] <- 0
glb_allobs_df[, paste0(feat, ".last10.log")] <- log(1 + last10)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last10.log")] > 0, ],
ycol_names=paste0(feat, ".last10.log"),
xcol_name=glb_rsp_var))
last100 <- as.numeric(merge(z-lag(z, -100), b, all=TRUE)); last100[is.na(last100)] <- 0
glb_allobs_df[, paste0(feat, ".last100.log")] <- log(1 + last100)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last100.log")] > 0, ],
ycol_names=paste0(feat, ".last100.log"),
xcol_name=glb_rsp_var))
glb_allobs_df <- orderBy(reformulate(glb_id_var), glb_allobs_df)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c(paste0(feat, ".zoo")))
# all2$last3 = as.numeric(merge(z-lag(z, -3), b, all = TRUE))
# all2$last5 = as.numeric(merge(z-lag(z, -5), b, all = TRUE))
# all2$last10 = as.numeric(merge(z-lag(z, -10), b, all = TRUE))
# all2$last20 = as.numeric(merge(z-lag(z, -20), b, all = TRUE))
# all2$last50 = as.numeric(merge(z-lag(z, -50), b, all = TRUE))
#
#
# # order table
# all2 = all2[order(all2$id),]
#
# ## fill in NAs
# # count averages
# na.avg = all2 %>% group_by(weekend, hour) %>% dplyr::summarise(
# last1=mean(last1, na.rm=TRUE),
# last3=mean(last3, na.rm=TRUE),
# last5=mean(last5, na.rm=TRUE),
# last10=mean(last10, na.rm=TRUE),
# last20=mean(last20, na.rm=TRUE),
# last50=mean(last50, na.rm=TRUE)
# )
#
# # fill in averages
# na.merge = merge(all2, na.avg, by=c("weekend","hour"))
# na.merge = na.merge[order(na.merge$id),]
# for(i in c("last1", "last3", "last5", "last10", "last20", "last50")) {
# y = paste0(i, ".y")
# idx = is.na(all2[[i]])
# all2[idx,][[i]] <- na.merge[idx,][[y]]
# }
# rm(na.avg, na.merge, b, i, idx, n, pd, sec, sh, y, z)
}
}
rm(last1, last10, last100)## Warning in rm(last1, last10, last100): object 'last1' not found## Warning in rm(last1, last10, last100): object 'last10' not found## Warning in rm(last1, last10, last100): object 'last100' not found# Create factors of string variables
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "factorize.str.vars"), major.inc=TRUE)## label step_major step_minor bgn end
## 1 extract.features_bgn 1 0 17.729 17.745
## 2 extract.features_factorize.str.vars 2 0 17.745 NA
## elapsed
## 1 0.016
## 2 NA#stop(here"); sav_allobs_df <- glb_allobs_df; #glb_allobs_df <- sav_allobs_df
print(str_vars <- myfind_chr_cols_df(glb_allobs_df))## description condition cellular carrier color
## "description" "condition" "cellular" "carrier" "color"
## storage productline .src prdline.my
## "storage" "productline" ".src" "prdline.my"if (length(str_vars <- setdiff(str_vars,
c(glb_exclude_vars_as_features, glb_txt_vars))) > 0) {
for (var in str_vars) {
warning("Creating factors of string variable: ", var,
": # of unique values: ", length(unique(glb_allobs_df[, var])))
glb_allobs_df[, paste0(var, ".fctr")] <-
relevel(factor(glb_allobs_df[, var]),
names(which.max(table(glb_allobs_df[, var], useNA = "ifany"))))
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, str_vars)
}## Warning: Creating factors of string variable: condition: # of unique
## values: 6## Warning: Creating factors of string variable: cellular: # of unique values:
## 3## Warning: Creating factors of string variable: carrier: # of unique values:
## 7## Warning: Creating factors of string variable: color: # of unique values: 5## Warning: Creating factors of string variable: storage: # of unique values:
## 5## Warning: Creating factors of string variable: prdline.my: # of unique
## values: 12if (!is.null(glb_txt_vars)) {
require(foreach)
require(gsubfn)
require(stringr)
require(tm)
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "process.text"), major.inc=TRUE)
chk_pattern_freq <- function(rex_str, ignore.case=TRUE) {
match_mtrx <- str_extract_all(txt_vctr, regex(rex_str, ignore_case=ignore.case),
simplify=TRUE)
match_df <- as.data.frame(match_mtrx[match_mtrx != ""])
names(match_df) <- "pattern"
return(mycreate_sqlxtab_df(match_df, "pattern"))
}
# match_lst <- gregexpr("\\bok(?!ay)", txt_vctr[746], ignore.case = FALSE, perl=TRUE); print(match_lst)
dsp_pattern <- function(rex_str, ignore.case=TRUE, print.all=TRUE) {
match_lst <- gregexpr(rex_str, txt_vctr, ignore.case = ignore.case, perl=TRUE)
match_lst <- regmatches(txt_vctr, match_lst)
match_df <- data.frame(matches=sapply(match_lst,
function (elems) paste(elems, collapse="#")))
match_df <- subset(match_df, matches != "")
if (print.all)
print(match_df)
return(match_df)
}
dsp_matches <- function(rex_str, ix) {
print(match_pos <- gregexpr(rex_str, txt_vctr[ix], perl=TRUE))
print(str_sub(txt_vctr[ix], (match_pos[[1]] / 100) * 99 + 0,
(match_pos[[1]] / 100) * 100 + 100))
}
myapply_gsub <- function(...) {
if ((length_lst <- length(names(gsub_map_lst))) == 0)
return(txt_vctr)
for (ptn_ix in 1:length_lst) {
if ((ptn_ix %% 10) == 0)
print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix,
length(names(gsub_map_lst)), names(gsub_map_lst)[ptn_ix]))
txt_vctr <- gsub(names(gsub_map_lst)[ptn_ix], gsub_map_lst[[ptn_ix]],
txt_vctr, ...)
}
return(txt_vctr)
}
myapply_txtmap <- function(txt_vctr, ...) {
nrows <- nrow(glb_txt_map_df)
for (ptn_ix in 1:nrows) {
if ((ptn_ix %% 10) == 0)
print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix,
nrows, glb_txt_map_df[ptn_ix, "rex_str"]))
txt_vctr <- gsub(glb_txt_map_df[ptn_ix, "rex_str"],
glb_txt_map_df[ptn_ix, "rpl_str"],
txt_vctr, ...)
}
return(txt_vctr)
}
chk.equal <- function(bgn, end) {
print(all.equal(sav_txt_lst[["Headline"]][bgn:end],
glb_txt_lst[["Headline"]][bgn:end]))
}
dsp.equal <- function(bgn, end) {
print(sav_txt_lst[["Headline"]][bgn:end])
print(glb_txt_lst[["Headline"]][bgn:end])
}
#sav_txt_lst <- glb_txt_lst; all.equal(sav_txt_lst, glb_txt_lst)
#all.equal(sav_txt_lst[["Headline"]][1:4200], glb_txt_lst[["Headline"]][1:4200])
#chk.equal( 1, 100)
#dsp.equal(86, 90)
txt_map_filename <- paste0(glb_txt_munge_filenames_pfx, "map.csv")
if (!file.exists(txt_map_filename))
stop(txt_map_filename, " not found!")
glb_txt_map_df <- read.csv(txt_map_filename, comment.char="#", strip.white=TRUE)
glb_txt_lst <- list();
print(sprintf("Building glb_txt_lst..."))
glb_txt_lst <- foreach(txt_var=glb_txt_vars) %dopar% {
# for (txt_var in glb_txt_vars) {
txt_vctr <- glb_allobs_df[, txt_var]
# myapply_txtmap shd be created as a tm_map::content_transformer ?
#print(glb_txt_map_df)
#txt_var=glb_txt_vars[3]; txt_vctr <- glb_txt_lst[[txt_var]]
#print(rex_str <- glb_txt_map_df[163, "rex_str"])
#print(rex_str <- glb_txt_map_df[glb_txt_map_df$rex_str == "\\bWall St\\.", "rex_str"])
#print(rex_str <- glb_txt_map_df[grepl("du Pont", glb_txt_map_df$rex_str), "rex_str"])
#print(rex_str <- glb_txt_map_df[glb_txt_map_df$rpl_str == "versus", "rex_str"])
#print(tmp_vctr <- grep(rex_str, txt_vctr, value=TRUE, ignore.case=FALSE))
#ret_lst <- regexec(rex_str, txt_vctr, ignore.case=FALSE); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
#gsub(rex_str, glb_txt_map_df[glb_txt_map_df$rex_str == rex_str, "rpl_str"], tmp_vctr, ignore.case=FALSE)
#grep("Hong Hong", txt_vctr, value=TRUE)
txt_vctr <- myapply_txtmap(txt_vctr, ignore.case=FALSE)
}
names(glb_txt_lst) <- glb_txt_vars
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining OK in %s:", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(chk_pattern_freq(rex_str <- "(?<!(BO|HO|LO))OK(?!(E\\!|ED|IE|IN|S ))",
ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
print(chk_pattern_freq(rex_str <- "Ok(?!(a\\.|ay|in|ra|um))", ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
print(chk_pattern_freq(rex_str <- "(?<!( b| B| c| C| g| G| j| M| p| P| w| W| r| Z|\\(b|ar|bo|Bo|co|Co|Ew|gk|go|ho|ig|jo|kb|ke|Ke|ki|lo|Lo|mo|mt|no|No|po|ra|ro|sm|Sm|Sp|to|To))ok(?!(ay|bo|e |e\\)|e,|e\\.|eb|ed|el|en|er|es|ey|i |ie|in|it|ka|ke|ki|ly|on|oy|ra|st|u |uc|uy|yl|yo))",
ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
}
# txt_vctr <- glb_txt_lst[[glb_txt_vars[1]]]
# print(chk_pattern_freq(rex_str <- "(?<!( b| c| C| p|\\(b|bo|co|lo|Lo|Sp|to|To))ok(?!(ay|e |e\\)|e,|e\\.|ed|el|en|es|ey|ie|in|on|ra))", ignore.case=FALSE))
# print(chk_pattern_freq(rex_str <- "ok(?!(ay|el|on|ra))", ignore.case=FALSE))
# dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
# dsp_matches(rex_str, ix=8)
# substr(txt_vctr[86], 5613, 5620)
# substr(glb_allobs_df[301, "review"], 550, 650)
#stop(here"); sav_txt_lst <- glb_txt_lst
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining Acronyms in %s:", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(chk_pattern_freq(rex_str <- "([[:upper:]]\\.( *)){2,}", ignore.case=FALSE))
# Check for names
print(subset(chk_pattern_freq(rex_str <- "(([[:upper:]]+)\\.( *)){1}",
ignore.case=FALSE),
.n > 1))
# dsp_pattern(rex_str="(OK\\.( *)){1}", ignore.case=FALSE)
# dsp_matches(rex_str="(OK\\.( *)){1}", ix=557)
#dsp_matches(rex_str="\\bR\\.I\\.P(\\.*)(\\B)", ix=461)
#dsp_matches(rex_str="\\bR\\.I\\.P(\\.*)", ix=461)
#print(str_sub(txt_vctr[676], 10100, 10200))
#print(str_sub(txt_vctr[74], 1, -1))
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(Fort|Ft\\.|Hong|Las|Los|New|Puerto|Saint|San|St\\.)( |-)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl("( |-)[[:upper:]]", pattern))))
print(" consider cleaning if relevant to problem domain; geography name; .n > 1")
#grep("New G", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("St\\. Wins", txt_vctr, value=TRUE, ignore.case=FALSE)
}
#stop(here"); sav_txt_lst <- glb_txt_lst
for (txt_var in glb_txt_vars) {
re_str <- "\\b(N|S|E|W|C)( |\\.)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl(".", pattern))))
#grep("N Weaver", txt_vctr, value=TRUE, ignore.case=FALSE)
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(North|South|East|West|Central)( |\\.)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
if (nrow(filtered_df <- subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl(".", pattern))) > 0)
print(orderBy(~ -.n +pattern, filtered_df))
#grep("Central (African|Bankers|Cast|Italy|Role|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("East (Africa|Berlin|London|Poland|Rivals|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("North (American|Korean|West)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("South (Pacific|Street)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("St\\. Martins", txt_vctr, value=TRUE, ignore.case=FALSE)
}
find_cmpnd_wrds <- function(txt_vctr) {
txt_corpus <- Corpus(VectorSource(txt_vctr))
txt_corpus <- tm_map(txt_corpus, content_transformer(tolower), lazy=TRUE)
txt_corpus <- tm_map(txt_corpus, PlainTextDocument, lazy=TRUE)
txt_corpus <- tm_map(txt_corpus, removePunctuation, lazy=TRUE,
preserve_intra_word_dashes=TRUE, lazy=TRUE)
full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTf))
print(" Full TermMatrix:"); print(full_Tf_DTM)
full_Tf_mtrx <- as.matrix(full_Tf_DTM)
rownames(full_Tf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
full_Tf_vctr <- colSums(full_Tf_mtrx)
names(full_Tf_vctr) <- dimnames(full_Tf_DTM)[[2]]
#grep("year", names(full_Tf_vctr), value=TRUE)
#which.max(full_Tf_mtrx[, "yearlong"])
full_Tf_df <- as.data.frame(full_Tf_vctr)
names(full_Tf_df) <- "Tf.full"
full_Tf_df$term <- rownames(full_Tf_df)
#full_Tf_df$freq.full <- colSums(full_Tf_mtrx != 0)
full_Tf_df <- orderBy(~ -Tf.full, full_Tf_df)
cmpnd_Tf_df <- full_Tf_df[grep("-", full_Tf_df$term, value=TRUE) ,]
txt_compound_filename <- paste0(glb_txt_munge_filenames_pfx, "compound.csv")
if (!file.exists(txt_compound_filename))
stop(txt_compound_filename, " not found!")
filter_df <- read.csv(txt_compound_filename, comment.char="#", strip.white=TRUE)
cmpnd_Tf_df$filter <- FALSE
for (row_ix in 1:nrow(filter_df))
cmpnd_Tf_df[!cmpnd_Tf_df$filter, "filter"] <-
grepl(filter_df[row_ix, "rex_str"],
cmpnd_Tf_df[!cmpnd_Tf_df$filter, "term"], ignore.case=TRUE)
cmpnd_Tf_df <- subset(cmpnd_Tf_df, !filter)
# Bug in tm_map(txt_corpus, removePunctuation, preserve_intra_word_dashes=TRUE) ???
# "net-a-porter" gets converted to "net-aporter"
#grep("net-a-porter", txt_vctr, ignore.case=TRUE, value=TRUE)
#grep("maser-laser", txt_vctr, ignore.case=TRUE, value=TRUE)
#txt_corpus[[which(grepl("net-a-porter", txt_vctr, ignore.case=TRUE))]]
#grep("\\b(across|longer)-(\\w)", cmpnd_Tf_df$term, ignore.case=TRUE, value=TRUE)
#grep("(\\w)-(affected|term)\\b", cmpnd_Tf_df$term, ignore.case=TRUE, value=TRUE)
print(sprintf("nrow(cmpnd_Tf_df): %d", nrow(cmpnd_Tf_df)))
myprint_df(cmpnd_Tf_df)
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "process.text_reporting_compound_terms"), major.inc=FALSE)
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining compound terms in %s: ", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
# find_cmpnd_wrds(txt_vctr)
#grep("thirty-five", txt_vctr, ignore.case=TRUE, value=TRUE)
#rex_str <- glb_txt_map_df[grepl("hirty", glb_txt_map_df$rex_str), "rex_str"]
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "build.corpus"), major.inc=TRUE)
glb_corpus_lst <- list()
print(sprintf("Building glb_corpus_lst..."))
glb_corpus_lst <- foreach(txt_var=glb_txt_vars) %dopar% {
# for (txt_var in glb_txt_vars) {
txt_corpus <- Corpus(VectorSource(glb_txt_lst[[txt_var]]))
txt_corpus <- tm_map(txt_corpus, content_transformer(tolower), lazy=TRUE) #nuppr
txt_corpus <- tm_map(txt_corpus, PlainTextDocument, lazy=TRUE)
txt_corpus <- tm_map(txt_corpus, removePunctuation, lazy=TRUE) #npnct<chr_ix>
# txt-corpus <- tm_map(txt_corpus, content_transformer(function(x, pattern) gsub(pattern, "", x))
# Not to be run in production
inspect_terms <- function() {
full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTf))
print(" Full TermMatrix:"); print(full_Tf_DTM)
full_Tf_mtrx <- as.matrix(full_Tf_DTM)
rownames(full_Tf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
full_Tf_vctr <- colSums(full_Tf_mtrx)
names(full_Tf_vctr) <- dimnames(full_Tf_DTM)[[2]]
#grep("year", names(full_Tf_vctr), value=TRUE)
#which.max(full_Tf_mtrx[, "yearlong"])
full_Tf_df <- as.data.frame(full_Tf_vctr)
names(full_Tf_df) <- "Tf.full"
full_Tf_df$term <- rownames(full_Tf_df)
#full_Tf_df$freq.full <- colSums(full_Tf_mtrx != 0)
full_Tf_df <- orderBy(~ -Tf.full +term, full_Tf_df)
print(myplot_histogram(full_Tf_df, "Tf.full"))
myprint_df(full_Tf_df)
#txt_corpus[[which(grepl("zun", txt_vctr, ignore.case=TRUE))]]
digit_terms_df <- subset(full_Tf_df, grepl("[[:digit:]]", term))
myprint_df(digit_terms_df)
return(full_Tf_df)
}
#print("RemovePunct:"); remove_punct_Tf_df <- inspect_terms()
txt_corpus <- tm_map(txt_corpus, removeWords,
c(glb_append_stop_words[[txt_var]],
stopwords("english")), lazy=TRUE) #nstopwrds
#print("StoppedWords:"); stopped_words_Tf_df <- inspect_terms()
txt_corpus <- tm_map(txt_corpus, stemDocument, lazy=TRUE) #Features for lost information: Difference/ratio in density of full_TfIdf_DTM ???
#txt_corpus <- tm_map(txt_corpus, content_transformer(stemDocument))
#print("StemmedWords:"); stemmed_words_Tf_df <- inspect_terms()
#stemmed_stopped_Tf_df <- merge(stemmed_words_Tf_df, stopped_words_Tf_df, by="term", all=TRUE, suffixes=c(".stem", ".stop"))
#myprint_df(stemmed_stopped_Tf_df)
#print(subset(stemmed_stopped_Tf_df, grepl("compan", term)))
#glb_corpus_lst[[txt_var]] <- txt_corpus
}
names(glb_corpus_lst) <- glb_txt_vars
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "extract.DTM"), major.inc=TRUE)
glb_full_DTM_lst <- list(); glb_sprs_DTM_lst <- list();
for (txt_var in glb_txt_vars) {
print(sprintf("Extracting TfIDf terms for %s...", txt_var))
txt_corpus <- glb_corpus_lst[[txt_var]]
# full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
# control=list(weighting=weightTf))
full_TfIdf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTfIdf))
sprs_TfIdf_DTM <- removeSparseTerms(full_TfIdf_DTM,
glb_sprs_thresholds[txt_var])
# glb_full_DTM_lst[[txt_var]] <- full_Tf_DTM
# glb_sprs_DTM_lst[[txt_var]] <- sprs_Tf_DTM
glb_full_DTM_lst[[txt_var]] <- full_TfIdf_DTM
glb_sprs_DTM_lst[[txt_var]] <- sprs_TfIdf_DTM
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "report.DTM"), major.inc=TRUE)
for (txt_var in glb_txt_vars) {
print(sprintf("Reporting TfIDf terms for %s...", txt_var))
full_TfIdf_DTM <- glb_full_DTM_lst[[txt_var]]
sprs_TfIdf_DTM <- glb_sprs_DTM_lst[[txt_var]]
print(" Full TermMatrix:"); print(full_TfIdf_DTM)
full_TfIdf_mtrx <- as.matrix(full_TfIdf_DTM)
rownames(full_TfIdf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
full_TfIdf_vctr <- colSums(full_TfIdf_mtrx)
names(full_TfIdf_vctr) <- dimnames(full_TfIdf_DTM)[[2]]
#grep("scene", names(full_TfIdf_vctr), value=TRUE)
#which.max(full_TfIdf_mtrx[, "yearlong"])
full_TfIdf_df <- as.data.frame(full_TfIdf_vctr)
names(full_TfIdf_df) <- "TfIdf.full"
full_TfIdf_df$term <- rownames(full_TfIdf_df)
full_TfIdf_df$freq.full <- colSums(full_TfIdf_mtrx != 0)
full_TfIdf_df <- orderBy(~ -TfIdf.full, full_TfIdf_df)
print(" Sparse TermMatrix:"); print(sprs_TfIdf_DTM)
sprs_TfIdf_vctr <- colSums(as.matrix(sprs_TfIdf_DTM))
names(sprs_TfIdf_vctr) <- dimnames(sprs_TfIdf_DTM)[[2]]
sprs_TfIdf_df <- as.data.frame(sprs_TfIdf_vctr)
names(sprs_TfIdf_df) <- "TfIdf.sprs"
sprs_TfIdf_df$term <- rownames(sprs_TfIdf_df)
sprs_TfIdf_df$freq.sprs <- colSums(as.matrix(sprs_TfIdf_DTM) != 0)
sprs_TfIdf_df <- orderBy(~ -TfIdf.sprs, sprs_TfIdf_df)
terms_TfIdf_df <- merge(full_TfIdf_df, sprs_TfIdf_df, all.x=TRUE)
terms_TfIdf_df$in.sprs <- !is.na(terms_TfIdf_df$freq.sprs)
plt_TfIdf_df <- subset(terms_TfIdf_df,
TfIdf.full >= min(terms_TfIdf_df$TfIdf.sprs, na.rm=TRUE))
plt_TfIdf_df$label <- ""
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "label"] <-
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "term"]
glb_important_terms[[txt_var]] <- union(glb_important_terms[[txt_var]],
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "term"])
print(myplot_scatter(plt_TfIdf_df, "freq.full", "TfIdf.full",
colorcol_name="in.sprs") +
geom_text(aes(label=label), color="Black", size=3.5))
melt_TfIdf_df <- orderBy(~ -value, melt(terms_TfIdf_df, id.var="term"))
print(ggplot(melt_TfIdf_df, aes(value, color=variable)) + stat_ecdf() +
geom_hline(yintercept=glb_sprs_thresholds[txt_var],
linetype = "dotted"))
melt_TfIdf_df <- orderBy(~ -value,
melt(subset(terms_TfIdf_df, !is.na(TfIdf.sprs)), id.var="term"))
print(myplot_hbar(melt_TfIdf_df, "term", "value",
colorcol_name="variable"))
melt_TfIdf_df <- orderBy(~ -value,
melt(subset(terms_TfIdf_df, is.na(TfIdf.sprs)), id.var="term"))
print(myplot_hbar(head(melt_TfIdf_df, 10), "term", "value",
colorcol_name="variable"))
}
# sav_full_DTM_lst <- glb_full_DTM_lst
# sav_sprs_DTM_lst <- glb_sprs_DTM_lst
# print(identical(sav_glb_corpus_lst, glb_corpus_lst))
# print(all.equal(length(sav_glb_corpus_lst), length(glb_corpus_lst)))
# print(all.equal(names(sav_glb_corpus_lst), names(glb_corpus_lst)))
# print(all.equal(sav_glb_corpus_lst[["Headline"]], glb_corpus_lst[["Headline"]]))
# print(identical(sav_full_DTM_lst, glb_full_DTM_lst))
# print(identical(sav_sprs_DTM_lst, glb_sprs_DTM_lst))
rm(full_TfIdf_mtrx, full_TfIdf_df, melt_TfIdf_df, terms_TfIdf_df)
# Create txt features
if ((length(glb_txt_vars) > 1) &&
(length(unique(pfxs <- sapply(glb_txt_vars,
function(txt) toupper(substr(txt, 1, 1))))) < length(glb_txt_vars)))
stop("Prefixes for corpus freq terms not unique: ", pfxs)
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "bind.DTM"),
major.inc=TRUE)
for (txt_var in glb_txt_vars) {
print(sprintf("Binding DTM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
txt_X_df <- as.data.frame(as.matrix(glb_sprs_DTM_lst[[txt_var]]))
colnames(txt_X_df) <- paste(txt_var_pfx, ".T.",
make.names(colnames(txt_X_df)), sep="")
rownames(txt_X_df) <- rownames(glb_allobs_df) # warning otherwise
# plt_X_df <- cbind(txt_X_df, glb_allobs_df[, c(glb_id_var, glb_rsp_var)])
# print(myplot_box(df=plt_X_df, ycol_names="H.T.today", xcol_name=glb_rsp_var))
# log_X_df <- log(1 + txt_X_df)
# colnames(log_X_df) <- paste(colnames(txt_X_df), ".log", sep="")
# plt_X_df <- cbind(log_X_df, glb_allobs_df[, c(glb_id_var, glb_rsp_var)])
# print(myplot_box(df=plt_X_df, ycol_names="H.T.today.log", xcol_name=glb_rsp_var))
glb_allobs_df <- cbind(glb_allobs_df, txt_X_df) # TfIdf is normalized
#glb_allobs_df <- cbind(glb_allobs_df, log_X_df) # if using non-normalized metrics
}
#identical(chk_entity_df, glb_allobs_df)
#chk_entity_df <- glb_allobs_df
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "bind.DXM"),
major.inc=TRUE)
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
glb_punct_vctr <- c("!", "\"", "#", "\\$", "%", "&", "'",
"\\(|\\)",# "\\(", "\\)",
"\\*", "\\+", ",", "-", "\\.", "/", ":", ";",
"<|>", # "<",
"=",
# ">",
"\\?", "@", "\\[", "\\\\", "\\]", "^", "_", "`",
"\\{", "\\|", "\\}", "~")
txt_X_df <- glb_allobs_df[, c(glb_id_var, ".rnorm"), FALSE]
txt_X_df <- foreach(txt_var=glb_txt_vars, .combine=cbind) %dopar% {
#for (txt_var in glb_txt_vars) {
print(sprintf("Binding DXM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
txt_full_DTM_mtrx <- as.matrix(glb_full_DTM_lst[[txt_var]])
rownames(txt_full_DTM_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
#print(txt_full_DTM_mtrx[txt_full_DTM_mtrx[, "ebola"] != 0, "ebola"])
# Create <txt_var>.T.<term> for glb_important_terms
for (term in glb_important_terms[[txt_var]])
txt_X_df[, paste0(txt_var_pfx, ".T.", make.names(term))] <-
txt_full_DTM_mtrx[, term]
# Create <txt_var>.nwrds.log & .nwrds.unq.log
txt_X_df[, paste0(txt_var_pfx, ".nwrds.log")] <-
log(1 + mycount_pattern_occ("\\w+", glb_txt_lst[[txt_var]]))
txt_X_df[, paste0(txt_var_pfx, ".nwrds.unq.log")] <-
log(1 + rowSums(txt_full_DTM_mtrx != 0))
txt_X_df[, paste0(txt_var_pfx, ".sum.TfIdf")] <-
rowSums(txt_full_DTM_mtrx)
txt_X_df[, paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")] <-
txt_X_df[, paste0(txt_var_pfx, ".sum.TfIdf")] /
(exp(txt_X_df[, paste0(txt_var_pfx, ".nwrds.log")]) - 1)
txt_X_df[is.nan(txt_X_df[, paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")]),
paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")] <- 0
# Create <txt_var>.nchrs.log
txt_X_df[, paste0(txt_var_pfx, ".nchrs.log")] <-
log(1 + mycount_pattern_occ(".", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".nuppr.log")] <-
log(1 + mycount_pattern_occ("[[:upper:]]", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".ndgts.log")] <-
log(1 + mycount_pattern_occ("[[:digit:]]", glb_allobs_df[, txt_var]))
# Create <txt_var>.npnct?.log
# would this be faster if it's iterated over each row instead of
# each created column ???
for (punct_ix in 1:length(glb_punct_vctr)) {
# smp0 <- " "
# smp1 <- "! \" # $ % & ' ( ) * + , - . / : ; < = > ? @ [ \ ] ^ _ ` { | } ~"
# smp2 <- paste(smp1, smp1, sep=" ")
# print(sprintf("Testing %s pattern:", glb_punct_vctr[punct_ix]))
# results <- mycount_pattern_occ(glb_punct_vctr[punct_ix], c(smp0, smp1, smp2))
# names(results) <- NULL; print(results)
txt_X_df[,
paste0(txt_var_pfx, ".npnct", sprintf("%02d", punct_ix), ".log")] <-
log(1 + mycount_pattern_occ(glb_punct_vctr[punct_ix],
glb_allobs_df[, txt_var]))
}
# print(head(glb_allobs_df[glb_allobs_df[, "A.npnct23.log"] > 0,
# c("UniqueID", "Popular", "Abstract", "A.npnct23.log")]))
# Create <txt_var>.nstopwrds.log & <txt_var>ratio.nstopwrds.nwrds
stop_words_rex_str <- paste0("\\b(", paste0(c(glb_append_stop_words[[txt_var]],
stopwords("english")), collapse="|"),
")\\b")
txt_X_df[, paste0(txt_var_pfx, ".nstopwrds", ".log")] <-
log(1 + mycount_pattern_occ(stop_words_rex_str, glb_txt_lst[[txt_var]]))
txt_X_df[, paste0(txt_var_pfx, ".ratio.nstopwrds.nwrds")] <-
exp(txt_X_df[, paste0(txt_var_pfx, ".nstopwrds", ".log")] -
txt_X_df[, paste0(txt_var_pfx, ".nwrds", ".log")])
# Create <txt_var>.P.http
txt_X_df[, paste(txt_var_pfx, ".P.http", sep="")] <-
as.integer(0 + mycount_pattern_occ("http", glb_allobs_df[, txt_var]))
# Create <txt_var>.P.mini & air
txt_X_df[, paste(txt_var_pfx, ".P.mini", sep="")] <-
as.integer(0 + mycount_pattern_occ("mini(?!m)", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df[, paste(txt_var_pfx, ".P.air", sep="")] <-
as.integer(0 + mycount_pattern_occ("(?<![fhp])air", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df <- subset(txt_X_df, select=-.rnorm)
txt_X_df <- txt_X_df[, -grep(glb_id_var, names(txt_X_df), fixed=TRUE), FALSE]
#glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
}
glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
#myplot_box(glb_allobs_df, "A.sum.TfIdf", glb_rsp_var)
# if (sum(is.na(glb_allobs_df$D.P.http)) > 0)
# stop("Why is this happening ?")
# Generate summaries
# print(summary(glb_allobs_df))
# print(sapply(names(glb_allobs_df), function(col) sum(is.na(glb_allobs_df[, col]))))
# print(summary(glb_trnobs_df))
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(summary(glb_newobs_df))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_txt_vars)
rm(log_X_df, txt_X_df)
}## Loading required package: stringr
## Loading required package: tm
## Loading required package: NLP
##
## Attaching package: 'NLP'
##
## The following object is masked from 'package:ggplot2':
##
## annotate## label step_major step_minor bgn end
## 2 extract.features_factorize.str.vars 2 0 17.745 18.139
## 3 extract.features_process.text 3 0 18.140 NA
## elapsed
## 2 0.394
## 3 NA
## [1] "Building glb_txt_lst..."
## [1] "running gsub for 10 (of 171): #\\bA\\.D\\.P\\.#..."
## [1] "running gsub for 20 (of 171): #\\bA\\.P\\.#..."
## [1] "running gsub for 30 (of 171): #\\bC\\.I\\.C\\.C\\.#..."
## [1] "running gsub for 40 (of 171): #\\bE\\.J\\. Manuel#..."
## [1] "running gsub for 50 (of 171): #\\bF\\.T\\.C\\.#..."
## [1] "running gsub for 60 (of 171): #\\bI\\.M\\.F\\.#..."
## [1] "running gsub for 70 (of 171): #\\bJ\\.F\\.K\\.#..."
## [1] "running gsub for 80 (of 171): #\\bM\\.&A\\.#..."
## [1] "running gsub for 90 (of 171): #\\bN\\.R\\.A\\.#..."
## [1] "running gsub for 100 (of 171): #\\bP\\.R\\.#..."
## [1] "running gsub for 110 (of 171): #\\bS\\.E\\.O\\.#..."
## [1] "running gsub for 120 (of 171): #\\bU\\.S\\.(A\\.)*#..."
## [1] "running gsub for 130 (of 171): #\\bW\\.M\\.D\\.#..."
## [1] "running gsub for 140 (of 171): #\\bLos( |-)((Angeles|Angeless)\\b)+#..."
## [1] "running gsub for 150 (of 171): #(\\w)-(Shanghai|USA)\\b#..."
## [1] "running gsub for 160 (of 171): #(one|two|three|four|five|six|seven|eight|nine)-#..."
## [1] "running gsub for 170 (of 171): #\\b[Ss]eventy-[Oo]ne\\b#..."
## [1] "Remaining OK in description:"
## pattern .n
## 1 OK 7
## [[1]]
## [1] 3
## attr(,"match.length")
## [1] 2
## attr(,"useBytes")
## [1] TRUE
## attr(,"capture.start")
##
## [1,] 0 0
## attr(,"capture.length")
##
## [1,] 0 0
## attr(,"capture.names")
## [1] "" ""
##
## [1] "ROKEN: Device has at least one or more problems: \nFor Parts or Repair"
## [[1]]
## [1] 3
## attr(,"match.length")
## [1] 2
## attr(,"useBytes")
## [1] TRUE
## attr(,"capture.start")
##
## [1,] 0 0
## attr(,"capture.length")
##
## [1,] 0 0
## attr(,"capture.names")
## [1] "" ""
##
## [1] "ROKEN DEVICE: Problem with Apple ID"
## [[1]]
## [1] 3
## attr(,"match.length")
## [1] 2
## attr(,"useBytes")
## [1] TRUE
## attr(,"capture.start")
##
## [1,] 0 0
## attr(,"capture.length")
##
## [1,] 0 0
## attr(,"capture.names")
## [1] "" ""
##
## [1] "ROKEN: Device has at least one or more problems: \nFor Parts or Repair"
## [[1]]
## [1] 3
## attr(,"match.length")
## [1] 2
## attr(,"useBytes")
## [1] TRUE
## attr(,"capture.start")
##
## [1,] 0 0
## attr(,"capture.length")
##
## [1,] 0 0
## attr(,"capture.names")
## [1] "" ""
##
## [1] "ROKEN: Device has at least one or more problems: \nFor Parts or Repair"
## [[1]]
## [1] 3
## attr(,"match.length")
## [1] 2
## attr(,"useBytes")
## [1] TRUE
## attr(,"capture.start")
##
## [1,] 0 0
## attr(,"capture.length")
##
## [1,] 0 0
## attr(,"capture.names")
## [1] "" ""
##
## [1] "ROKEN: Device has at least one or more problems: \nFor Parts or Repair"
## [[1]]
## [1] 3
## attr(,"match.length")
## [1] 2
## attr(,"useBytes")
## [1] TRUE
## attr(,"capture.start")
##
## [1,] 0 0
## attr(,"capture.length")
##
## [1,] 0 0
## attr(,"capture.names")
## [1] "" ""
##
## [1] "ROKEN: Device has at least one or more problems: \nFor Parts or Repair"
## [[1]]
## [1] 3
## attr(,"match.length")
## [1] 2
## attr(,"useBytes")
## [1] TRUE
## attr(,"capture.start")
##
## [1,] 0 0
## attr(,"capture.length")
##
## [1,] 0 0
## attr(,"capture.names")
## [1] "" ""
##
## [1] "ROKEN SCREEN"
## [1] pattern .n
## <0 rows> (or 0-length row.names)
## [1] pattern .n
## <0 rows> (or 0-length row.names)
## [1] "Remaining Acronyms in description:"
## [1] pattern .n
## <0 rows> (or 0-length row.names)
## pattern .n
## 1 ONLY. 7
## 2 CONDITION. 6
## 3 GB. 4
## 4 BOX. 2
## 5 CONDITION. 2
## 6 CORNER. 2
## 7 ESN. 2
## 8 GOOD. 2
## 9 ICLOUD. 2
## 10 IMEI. 2
## 11 IPADS. 2
## 12 LOCKED. 2
## 13 LOCKS. 2
## 14 ON. 2
## 15 SCRATCHES. 2
## 16 TEARS. 2
## 17 USE. 2
## [1] "Remaining #\\b(Fort|Ft\\.|Hong|Las|Los|New|Puerto|Saint|San|St\\.)( |-)(\\w)+# terms in description: "
## pattern .n
## 2 New Open 3
## 4 New Condition 2
## 7 New Digitizer 1
## 8 New Opened 1
## 9 New Scratch 1
## 10 New Screen 1
## 14 New-Other 1
## [1] " consider cleaning if relevant to problem domain; geography name; .n > 1"
## [1] "Remaining #\\b(N|S|E|W|C)( |\\.)(\\w)+# terms in description: "
## pattern .n
## 1 C Stock 3
## 2 W blue 1
## [1] "Remaining #\\b(North|South|East|West|Central)( |\\.)(\\w)+# terms in description: "
## label step_major
## 3 extract.features_process.text 3
## 4 extract.features_process.text_reporting_compound_terms 3
## step_minor bgn end elapsed
## 3 0 18.140 20.51 2.37
## 4 1 20.511 NA NA
## [1] "Remaining compound terms in description: "
## label step_major
## 4 extract.features_process.text_reporting_compound_terms 3
## 5 extract.features_build.corpus 4
## step_minor bgn end elapsed
## 4 1 20.511 20.515 0.004
## 5 0 20.516 NA NA
## [1] "Building glb_corpus_lst..."
## label step_major step_minor bgn end
## 5 extract.features_build.corpus 4 0 20.516 21.137
## 6 extract.features_extract.DTM 5 0 21.137 NA
## elapsed
## 5 0.621
## 6 NA
## [1] "Extracting TfIDf terms for description..."## Warning in weighting(x): empty document(s): character(0) character(0)
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## character(0) charact## label step_major step_minor bgn end elapsed
## 6 extract.features_extract.DTM 5 0 21.137 24.577 3.44
## 7 extract.features_report.DTM 6 0 24.577 NA NA
## [1] "Reporting TfIDf terms for description..."
## [1] " Full TermMatrix:"
## <<DocumentTermMatrix (documents: 2659, terms: 1123)>>
## Non-/sparse entries: 9175/2976882
## Sparsity : 100%
## Maximal term length: 27
## Weighting : term frequency - inverse document frequency (normalized) (tf-idf)
## [1] " Sparse TermMatrix:"
## <<DocumentTermMatrix (documents: 2659, terms: 10)>>
## Non-/sparse entries: 1989/24601
## Sparsity : 93%
## Maximal term length: 9
## Weighting : term frequency - inverse document frequency (normalized) (tf-idf)## Warning in myplot_scatter(plt_TfIdf_df, "freq.full", "TfIdf.full",
## colorcol_name = "in.sprs"): converting in.sprs to class:factor
## Warning: Removed 6 rows containing missing values (geom_path).## Warning: Removed 6 rows containing missing values (geom_path).
## label step_major step_minor bgn end elapsed
## 7 extract.features_report.DTM 6 0 24.577 27.133 2.556
## 8 extract.features_bind.DTM 7 0 27.133 NA NA
## [1] "Binding DTM for description..."
## label step_major step_minor bgn end elapsed
## 8 extract.features_bind.DTM 7 0 27.133 27.142 0.009
## 9 extract.features_bind.DXM 8 0 27.142 NA NA
## [1] "Binding DXM for description..."## Warning in rm(log_X_df, txt_X_df): object 'log_X_df' not found
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
# dsp_obs(list(description.contains="mini(?!m)"), perl=TRUE, cols="D.P.mini", all=TRUE)
# dsp_obs(list(D.P.mini=1), cols="D.P.mini", all=TRUE)
# dsp_obs(list(D.P.mini=1, productline="Unknown"), cols="D.P.mini", all=TRUE)
# dsp_obs(list(description.contains="(?<![fhp])air"), perl=TRUE, all=TRUE)
# dsp_obs(list(description.contains="air"), perl=FALSE, cols="D.P.air", all=TRUE)
# dsp_obs(list(D.P.air=1, productline="Unknown"), cols="D.P.air", all=TRUE)
glb_allobs_df[(glb_allobs_df$D.P.mini == 1) & (glb_allobs_df$productline == "Unknown"), "prdline.my"] <- "iPad mini"
print(mycreate_sqlxtab_df(glb_allobs_df, c("prdline.my", "productline", "D.P.mini", glb_rsp_var)))## prdline.my productline D.P.mini sold.fctr .n
## 1 iPad 2 iPad 2 0 <NA> 154
## 2 iPad 2 iPad 2 0 Y 147
## 3 iPad 2 iPad 2 0 N 139
## 4 iPad mini iPad mini 0 N 138
## 5 iPad mini iPad mini 0 Y 126
## 6 iPad 1 iPad 1 0 Y 125
## 7 Unknown Unknown 0 N 121
## 8 iPad mini iPad mini 0 <NA> 108
## 9 iPad 1 iPad 1 0 N 102
## 10 iPad Air iPad Air 0 N 102
## 11 iPad Air 2 iPad Air 2 0 N 100
## 12 iPad 4 iPad 4 0 N 93
## 13 Unknown Unknown 0 <NA> 89
## 14 iPad 1 iPad 1 0 <NA> 88
## 15 Unknown Unknown 0 Y 81
## 16 iPad 3 iPad 3 0 Y 80
## 17 iPad Air iPad Air 0 Y 78
## 18 iPad Air iPad Air 0 <NA> 74
## 19 iPad 3 iPad 3 0 N 73
## 20 iPad Air 2 iPad Air 2 0 Y 71
## 21 iPad 4 iPad 4 0 <NA> 68
## 22 iPad 4 iPad 4 0 Y 64
## 23 iPad Air 2 iPad Air 2 0 <NA> 62
## 24 iPad mini 3 iPad mini 3 0 N 61
## 25 iPad mini 2 iPad mini 2 0 N 56
## 26 iPad 3 iPad 3 0 <NA> 55
## 27 iPad mini 2 iPad mini 2 0 <NA> 52
## 28 iPad mini 2 iPad mini 2 0 Y 48
## 29 iPad mini 3 iPad mini 3 0 <NA> 35
## 30 iPad mini 3 iPad mini 3 0 Y 27
## 31 iPad mini iPad mini 1 N 7
## 32 iPad mini iPad mini 1 Y 5
## 33 iPad mini 2 iPad mini 2 1 <NA> 4
## 34 iPad mini Retina iPad mini Retina 0 Y 4
## 35 iPad mini Unknown 1 <NA> 3
## 36 iPad mini iPad mini 1 <NA> 3
## 37 iPad mini 3 iPad mini 3 1 <NA> 3
## 38 iPad mini Retina iPad mini Retina 0 N 3
## 39 iPad mini 2 iPad mini 2 1 N 2
## 40 iPad mini 3 iPad mini 3 1 N 2
## 41 iPad 5 iPad 5 0 Y 1
## 42 iPad mini Unknown 1 N 1
## 43 iPad mini Unknown 1 Y 1
## 44 iPad mini iPad mini 2 Y 1
## 45 iPad mini 2 iPad mini 2 1 Y 1
## 46 iPad mini Retina iPad mini Retina 1 N 1glb_allobs_df[glb_allobs_df$UniqueID == 11863, "D.P.air"] <- 0
glb_allobs_df[(glb_allobs_df$D.P.air == 1) & (glb_allobs_df$productline == "Unknown"), "prdline.my"] <- "iPad Air"
print(mycreate_sqlxtab_df(glb_allobs_df, c("prdline.my", "productline", "D.P.air", glb_rsp_var)))## prdline.my productline D.P.air sold.fctr .n
## 1 iPad 2 iPad 2 0 <NA> 154
## 2 iPad 2 iPad 2 0 Y 147
## 3 iPad mini iPad mini 0 N 145
## 4 iPad 2 iPad 2 0 N 139
## 5 iPad mini iPad mini 0 Y 132
## 6 iPad 1 iPad 1 0 Y 125
## 7 Unknown Unknown 0 N 120
## 8 iPad mini iPad mini 0 <NA> 111
## 9 iPad 1 iPad 1 0 N 102
## 10 iPad Air iPad Air 0 N 98
## 11 iPad Air 2 iPad Air 2 0 N 97
## 12 iPad 4 iPad 4 0 N 92
## 13 Unknown Unknown 0 <NA> 88
## 14 iPad 1 iPad 1 0 <NA> 88
## 15 Unknown Unknown 0 Y 80
## 16 iPad 3 iPad 3 0 Y 79
## 17 iPad Air iPad Air 0 Y 75
## 18 iPad 3 iPad 3 0 N 73
## 19 iPad Air iPad Air 0 <NA> 73
## 20 iPad Air 2 iPad Air 2 0 Y 69
## 21 iPad 4 iPad 4 0 <NA> 68
## 22 iPad 4 iPad 4 0 Y 64
## 23 iPad mini 3 iPad mini 3 0 N 63
## 24 iPad Air 2 iPad Air 2 0 <NA> 60
## 25 iPad mini 2 iPad mini 2 0 N 58
## 26 iPad mini 2 iPad mini 2 0 <NA> 56
## 27 iPad 3 iPad 3 0 <NA> 55
## 28 iPad mini 2 iPad mini 2 0 Y 49
## 29 iPad mini 3 iPad mini 3 0 <NA> 38
## 30 iPad mini 3 iPad mini 3 0 Y 27
## 31 iPad Air iPad Air 1 N 4
## 32 iPad mini Retina iPad mini Retina 0 N 4
## 33 iPad mini Retina iPad mini Retina 0 Y 4
## 34 iPad Air iPad Air 1 Y 3
## 35 iPad mini Unknown 0 <NA> 3
## 36 iPad Air 2 iPad Air 2 1 <NA> 2
## 37 iPad Air 2 iPad Air 2 1 N 2
## 38 iPad Air 2 iPad Air 2 1 Y 2
## 39 iPad 3 iPad 3 1 Y 1
## 40 iPad 4 iPad 4 1 N 1
## 41 iPad 5 iPad 5 0 Y 1
## 42 iPad Air Unknown 1 <NA> 1
## 43 iPad Air Unknown 1 N 1
## 44 iPad Air Unknown 1 Y 1
## 45 iPad Air iPad Air 1 <NA> 1
## 46 iPad Air 2 iPad Air 2 2 N 1
## 47 iPad mini Unknown 0 N 1
## 48 iPad mini Unknown 0 Y 1glb_allobs_df[glb_allobs_df$UniqueID == 12156, "prdline.my"] <- "iPad 1"
glb_allobs_df[glb_allobs_df$UniqueID == 11811, "prdline.my"] <- "iPad 2"
glb_allobs_df[glb_allobs_df$UniqueID == 11767, "prdline.my"] <- "iPad 2"
glb_allobs_df[glb_allobs_df$UniqueID == 11767, "storage"] <- "32"
# dsp_obs(list(prdline.my="Unknown"), all=TRUE)
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
tmp_allobs_df <- glb_allobs_df[, "prdline.my", FALSE]
names(tmp_allobs_df) <- "old.prdline.my"
glb_allobs_df$prdline.my <-
plyr::revalue(glb_allobs_df$prdline.my, c(
# "iPad 1" = "iPad",
# "iPad 2" = "iPad2+",
"iPad 3" = "iPad 3+",
"iPad 4" = "iPad 3+",
"iPad 5" = "iPad 3+",
"iPad Air" = "iPadAir",
"iPad Air 2" = "iPadAir",
"iPad mini" = "iPadmini",
"iPad mini 2" = "iPadmini 2+",
"iPad mini 3" = "iPadmini 2+",
"iPad mini Retina" = "iPadmini 2+"
))
tmp_allobs_df$prdline.my <- glb_allobs_df[, "prdline.my"]
print(mycreate_sqlxtab_df(tmp_allobs_df, c("prdline.my", "old.prdline.my")))## prdline.my old.prdline.my .n
## 1 iPad 2 iPad 2 442
## 2 iPadmini iPad mini 393
## 3 iPad 1 iPad 1 316
## 4 Unknown Unknown 285
## 5 iPadAir iPad Air 257
## 6 iPadAir iPad Air 2 233
## 7 iPad 3+ iPad 4 225
## 8 iPad 3+ iPad 3 208
## 9 iPadmini 2+ iPad mini 2 163
## 10 iPadmini 2+ iPad mini 3 128
## 11 iPadmini 2+ iPad mini Retina 8
## 12 iPad 3+ iPad 5 1print(mycreate_sqlxtab_df(tmp_allobs_df, c("prdline.my")))## prdline.my .n
## 1 iPadAir 490
## 2 iPad 2 442
## 3 iPad 3+ 434
## 4 iPadmini 393
## 5 iPad 1 316
## 6 iPadmini 2+ 299
## 7 Unknown 285glb_allobs_df$prdline.my.fctr <- as.factor(glb_allobs_df$prdline.my)
glb_allobs_df$storage.fctr <- as.factor(glb_allobs_df$storage)
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# print(myplot_scatter(glb_trnobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
glb_full_DTM_lst, glb_sprs_DTM_lst, txt_corpus, txt_vctr)## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'corpus_lst' not foundextract.features_chunk_df <- myadd_chunk(extract.features_chunk_df, "extract.features_end",
major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 9 extract.features_bind.DXM 8 0 27.142 37.787 10.645
## 10 extract.features_end 9 0 37.787 NA NAmyplt_chunk(extract.features_chunk_df)## label step_major
## 9 extract.features_bind.DXM 8
## 6 extract.features_extract.DTM 5
## 7 extract.features_report.DTM 6
## 3 extract.features_process.text 3
## 5 extract.features_build.corpus 4
## 2 extract.features_factorize.str.vars 2
## 1 extract.features_bgn 1
## 8 extract.features_bind.DTM 7
## 4 extract.features_process.text_reporting_compound_terms 3
## step_minor bgn end elapsed duration
## 9 0 27.142 37.787 10.645 10.645
## 6 0 21.137 24.577 3.440 3.440
## 7 0 24.577 27.133 2.556 2.556
## 3 0 18.140 20.510 2.370 2.370
## 5 0 20.516 21.137 0.621 0.621
## 2 0 17.745 18.139 0.394 0.394
## 1 0 17.729 17.745 0.016 0.016
## 8 0 27.133 27.142 0.009 0.009
## 4 1 20.511 20.515 0.004 0.004
## [1] "Total Elapsed Time: 37.787 secs"
# if (glb_save_envir)
# save(glb_feats_df,
# glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
# file=paste0(glb_out_pfx, "extract_features_dsk.RData"))
# load(paste0(glb_out_pfx, "extract_features_dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all","data.new")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "cluster.data", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 5 extract.features 3 0 17.722 39.149 21.427
## 6 cluster.data 4 0 39.150 NA NAStep 4.0: cluster data
glb_chunks_df <- myadd_chunk(glb_chunks_df, "manage.missing.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 6 cluster.data 4 0 39.150 40.071 0.922
## 7 manage.missing.data 4 1 40.072 NA NA# If mice crashes with error: Error in get(as.character(FUN), mode = "function", envir = envir) : object 'State' of mode 'function' was not found
# consider excluding 'State' as a feature
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# glb_trnobs_df <- na.omit(glb_trnobs_df)
# glb_newobs_df <- na.omit(glb_newobs_df)
# df[is.na(df)] <- 0
mycheck_problem_data(glb_allobs_df)## [1] "numeric data missing in : "
## sold sold.fctr
## 798 798
## [1] "numeric data w/ 0s in : "
## biddable sold startprice.log
## 1446 1001 31
## cellular.fctr D.T.condit D.T.condition
## 1599 2455 2385
## D.T.good D.T.ipad D.T.new
## 2463 2432 2520
## D.T.scratch D.T.screen D.T.this
## 2431 2453 2503
## D.T.use D.T.work D.T.box
## 2474 2485 2553
## D.T.excellent D.T.great D.T.like
## 2602 2539 2591
## D.T.used D.T.veri D.nwrds.log
## 2540 2567 1521
## D.nwrds.unq.log D.sum.TfIdf D.ratio.sum.TfIdf.nwrds
## 1529 1529 1529
## D.nchrs.log D.nuppr.log D.ndgts.log
## 1521 1523 2428
## D.npnct01.log D.npnct02.log D.npnct03.log
## 2581 2659 2616
## D.npnct04.log D.npnct05.log D.npnct06.log
## 2659 2594 2556
## D.npnct07.log D.npnct08.log D.npnct09.log
## 2659 2583 2643
## D.npnct10.log D.npnct11.log D.npnct12.log
## 2650 2303 2539
## D.npnct13.log D.npnct14.log D.npnct15.log
## 1934 2584 2638
## D.npnct16.log D.npnct17.log D.npnct18.log
## 2548 2659 2658
## D.npnct19.log D.npnct20.log D.npnct21.log
## 2659 2659 2659
## D.npnct22.log D.npnct23.log D.npnct24.log
## 2659 2659 1521
## D.npnct25.log D.npnct26.log D.npnct27.log
## 2659 2659 2659
## D.npnct28.log D.npnct29.log D.npnct30.log
## 2651 2659 2659
## D.nstopwrds.log D.P.http D.P.mini
## 1669 2659 2625
## D.P.air
## 2639
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1521 0 0 0 0 0
## productline prdline.my
## 0 0# glb_allobs_df <- na.omit(glb_allobs_df)
# Not refactored into mydsutils.R since glb_*_df might be reassigned
glb_impute_missing_data <- function() {
require(mice)
set.seed(glb_mice_complete.seed)
inp_impent_df <- glb_allobs_df[, setdiff(names(glb_allobs_df),
union(glb_exclude_vars_as_features, glb_rsp_var))]
print("Summary before imputation: ")
print(summary(inp_impent_df))
out_impent_df <- complete(mice(inp_impent_df))
print(summary(out_impent_df))
ret_vars <- sapply(names(out_impent_df),
function(col) ifelse(!identical(out_impent_df[, col],
inp_impent_df[, col]),
col, ""))
ret_vars <- ret_vars[ret_vars != ""]
# complete(mice()) changes attributes of factors even though values don't change
for (col in ret_vars) {
if (inherits(out_impent_df[, col], "factor")) {
if (identical(as.numeric(out_impent_df[, col]),
as.numeric(inp_impent_df[, col])))
ret_vars <- setdiff(ret_vars, col)
}
}
return(out_impent_df[, ret_vars])
}
if (glb_impute_na_data &&
(length(myfind_numerics_missing(glb_allobs_df)) > 0) &&
(ncol(nonna_df <- glb_impute_missing_data()) > 0)) {
for (col in names(nonna_df)) {
glb_allobs_df[, paste0(col, ".nonNA")] <- nonna_df[, col]
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features, col)
}
}
mycheck_problem_data(glb_allobs_df, terminate = TRUE)## [1] "numeric data missing in : "
## sold sold.fctr
## 798 798
## [1] "numeric data w/ 0s in : "
## biddable sold startprice.log
## 1446 1001 31
## cellular.fctr D.T.condit D.T.condition
## 1599 2455 2385
## D.T.good D.T.ipad D.T.new
## 2463 2432 2520
## D.T.scratch D.T.screen D.T.this
## 2431 2453 2503
## D.T.use D.T.work D.T.box
## 2474 2485 2553
## D.T.excellent D.T.great D.T.like
## 2602 2539 2591
## D.T.used D.T.veri D.nwrds.log
## 2540 2567 1521
## D.nwrds.unq.log D.sum.TfIdf D.ratio.sum.TfIdf.nwrds
## 1529 1529 1529
## D.nchrs.log D.nuppr.log D.ndgts.log
## 1521 1523 2428
## D.npnct01.log D.npnct02.log D.npnct03.log
## 2581 2659 2616
## D.npnct04.log D.npnct05.log D.npnct06.log
## 2659 2594 2556
## D.npnct07.log D.npnct08.log D.npnct09.log
## 2659 2583 2643
## D.npnct10.log D.npnct11.log D.npnct12.log
## 2650 2303 2539
## D.npnct13.log D.npnct14.log D.npnct15.log
## 1934 2584 2638
## D.npnct16.log D.npnct17.log D.npnct18.log
## 2548 2659 2658
## D.npnct19.log D.npnct20.log D.npnct21.log
## 2659 2659 2659
## D.npnct22.log D.npnct23.log D.npnct24.log
## 2659 2659 1521
## D.npnct25.log D.npnct26.log D.npnct27.log
## 2659 2659 2659
## D.npnct28.log D.npnct29.log D.npnct30.log
## 2651 2659 2659
## D.nstopwrds.log D.P.http D.P.mini
## 1669 2659 2625
## D.P.air
## 2639
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1521 0 0 0 0 0
## productline prdline.my
## 0 0Step 4.1: manage missing data
if (glb_cluster) {
require(proxy)
#require(hash)
require(dynamicTreeCut)
# glb_hash <- hash(key=unique(glb_allobs_df$myCategory),
# values=1:length(unique(glb_allobs_df$myCategory)))
# glb_hash_lst <- hash(key=unique(glb_allobs_df$myCategory),
# values=1:length(unique(glb_allobs_df$myCategory)))
#stophere; sav_allobs_df <- glb_allobs_df;
print("Clustering features: ")
print(cluster_vars <- grep("[HSA]\\.[PT]\\.", names(glb_allobs_df), value=TRUE))
#print(cluster_vars <- grep("[HSA]\\.", names(glb_allobs_df), value=TRUE))
glb_allobs_df$.clusterid <- 1
#print(max(table(glb_allobs_df$myCategory.fctr) / 20))
for (myCategory in c("##", "Business#Business Day#Dealbook", "OpEd#Opinion#",
"Styles#U.S.#", "Business#Technology#", "Science#Health#",
"Culture#Arts#")) {
ctgry_allobs_df <- glb_allobs_df[glb_allobs_df$myCategory == myCategory, ]
dstns_dist <- dist(ctgry_allobs_df[, cluster_vars], method = "cosine")
dstns_mtrx <- as.matrix(dstns_dist)
print(sprintf("max distance(%0.4f) pair:", max(dstns_mtrx)))
row_ix <- ceiling(which.max(dstns_mtrx) / ncol(dstns_mtrx))
col_ix <- which.max(dstns_mtrx[row_ix, ])
print(ctgry_allobs_df[c(row_ix, col_ix),
c("UniqueID", "Popular", "myCategory", "Headline", cluster_vars)])
min_dstns_mtrx <- dstns_mtrx
diag(min_dstns_mtrx) <- 1
print(sprintf("min distance(%0.4f) pair:", min(min_dstns_mtrx)))
row_ix <- ceiling(which.min(min_dstns_mtrx) / ncol(min_dstns_mtrx))
col_ix <- which.min(min_dstns_mtrx[row_ix, ])
print(ctgry_allobs_df[c(row_ix, col_ix),
c("UniqueID", "Popular", "myCategory", "Headline", cluster_vars)])
clusters <- hclust(dstns_dist, method = "ward.D2")
#plot(clusters, labels=NULL, hang=-1)
myplclust(clusters, lab.col=unclass(ctgry_allobs_df[, glb_rsp_var]))
#clusterGroups = cutree(clusters, k=7)
clusterGroups <- cutreeDynamic(clusters, minClusterSize=20, method="tree", deepSplit=0)
# Unassigned groups are labeled 0; the largest group has label 1
table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
#print(ctgry_allobs_df[which(clusterGroups == 1), c("UniqueID", "Popular", "Headline")])
#print(ctgry_allobs_df[(clusterGroups == 1) & !is.na(ctgry_allobs_df$Popular) & (ctgry_allobs_df$Popular==1), c("UniqueID", "Popular", "Headline")])
clusterGroups[clusterGroups == 0] <- 1
table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
#summary(factor(clusterGroups))
# clusterGroups <- clusterGroups +
# 100 * # has to be > max(table(glb_allobs_df$myCategory.fctr) / minClusterSize=20)
# which(levels(glb_allobs_df$myCategory.fctr) == myCategory)
# table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
# add to glb_allobs_df - then split the data again
glb_allobs_df[glb_allobs_df$myCategory==myCategory,]$.clusterid <- clusterGroups
#print(unique(glb_allobs_df$.clusterid))
#print(glb_feats_df[glb_feats_df$id == ".clusterid.fctr", ])
}
ctgry_xtab_df <- orderBy(reformulate(c("-", ".n")),
mycreate_sqlxtab_df(glb_allobs_df,
c("myCategory", ".clusterid", glb_rsp_var)))
ctgry_cast_df <- orderBy(~ -Y -NA, dcast(ctgry_xtab_df,
myCategory + .clusterid ~
Popular.fctr, sum, value.var=".n"))
print(ctgry_cast_df)
#print(orderBy(~ myCategory -Y -NA, ctgry_cast_df))
# write.table(ctgry_cast_df, paste0(glb_out_pfx, "ctgry_clst.csv"),
# row.names=FALSE)
print(ctgry_sum_tbl <- table(glb_allobs_df$myCategory, glb_allobs_df$.clusterid,
glb_allobs_df[, glb_rsp_var],
useNA="ifany"))
# dsp_obs(.clusterid=1, myCategory="OpEd#Opinion#",
# cols=c("UniqueID", "Popular", "myCategory", ".clusterid", "Headline"),
# all=TRUE)
glb_allobs_df$.clusterid.fctr <- as.factor(glb_allobs_df$.clusterid)
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
".clusterid")
glb_interaction_only_features["myCategory.fctr"] <- c(".clusterid.fctr")
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
cluster_vars)
}
# Last call for data modifications
#stop(here") # sav_allobs_df <- glb_allobs_df
# glb_allobs_df[(glb_allobs_df$PropR == 0.75) & (glb_allobs_df$State == "Hawaii"), "PropR.fctr"] <- "N"
# Re-partition
glb_trnobs_df <- subset(glb_allobs_df, .src == "Train")
glb_newobs_df <- subset(glb_allobs_df, .src == "Test")
glb_chunks_df <- myadd_chunk(glb_chunks_df, "select.features", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 7 manage.missing.data 4 1 40.072 40.216 0.144
## 8 select.features 5 0 40.217 NA NAStep 5.0: select features
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
print(glb_feats_df <- myselect_features(entity_df=glb_trnobs_df,
exclude_vars_as_features=glb_exclude_vars_as_features,
rsp_var=glb_rsp_var))## Warning in cor(data.matrix(entity_df[, sel_feats]), y =
## as.numeric(entity_df[, : the standard deviation is zero## id cor.y
## sold sold 1.0000000000
## biddable biddable 0.5485860292
## startprice.log startprice.log -0.4668597069
## startprice startprice -0.4556701005
## UniqueID UniqueID -0.1904241613
## condition.fctr condition.fctr -0.1531007887
## D.npnct05.log D.npnct05.log -0.1178427734
## prdline.my.fctr prdline.my.fctr -0.0871560229
## D.npnct14.log D.npnct14.log -0.0784119354
## cellular.fctr cellular.fctr -0.0722383245
## carrier.fctr carrier.fctr -0.0677807650
## D.ndgts.log D.ndgts.log -0.0633901377
## D.nwrds.unq.log D.nwrds.unq.log -0.0619524203
## D.npnct09.log D.npnct09.log -0.0617301891
## D.nwrds.log D.nwrds.log -0.0587159086
## D.nchrs.log D.nchrs.log -0.0565815056
## D.ratio.nstopwrds.nwrds D.ratio.nstopwrds.nwrds 0.0554866401
## D.nuppr.log D.nuppr.log -0.0554742013
## D.npnct12.log D.npnct12.log -0.0549329137
## D.T.like D.T.like -0.0533938586
## D.npnct28.log D.npnct28.log -0.0523776347
## D.T.box D.T.box -0.0515596986
## D.npnct06.log D.npnct06.log -0.0497583609
## D.T.this D.T.this -0.0477867670
## D.npnct24.log D.npnct24.log -0.0459503420
## D.npnct16.log D.npnct16.log -0.0447153098
## D.nstopwrds.log D.nstopwrds.log -0.0439279746
## color.fctr color.fctr -0.0426291775
## D.T.condit D.T.condit -0.0415362400
## D.npnct15.log D.npnct15.log 0.0400921803
## D.npnct08.log D.npnct08.log -0.0394718187
## D.npnct13.log D.npnct13.log -0.0349433657
## D.T.screen D.T.screen 0.0312904991
## D.T.new D.T.new -0.0302966020
## D.sum.TfIdf D.sum.TfIdf -0.0300344031
## D.npnct03.log D.npnct03.log 0.0258721719
## D.T.used D.T.used 0.0251046087
## D.npnct10.log D.npnct10.log -0.0240301079
## D.T.ipad D.T.ipad -0.0234288421
## .rnorm .rnorm -0.0228561937
## D.npnct18.log D.npnct18.log -0.0214919447
## D.T.excellent D.T.excellent 0.0203632794
## D.npnct11.log D.npnct11.log -0.0188179512
## D.T.great D.T.great 0.0171206415
## D.ratio.sum.TfIdf.nwrds D.ratio.sum.TfIdf.nwrds 0.0119528209
## D.T.condition D.T.condition -0.0118174892
## D.P.mini D.P.mini -0.0111321924
## storage.fctr storage.fctr -0.0103459049
## D.T.use D.T.use -0.0097129508
## D.P.air D.P.air -0.0091681483
## D.T.scratch D.T.scratch -0.0091382072
## D.T.good D.T.good 0.0063426895
## D.npnct01.log D.npnct01.log 0.0042941114
## D.T.work D.T.work -0.0035586740
## D.T.veri D.T.veri -0.0001659508
## D.npnct02.log D.npnct02.log NA
## D.npnct04.log D.npnct04.log NA
## D.npnct07.log D.npnct07.log NA
## D.npnct17.log D.npnct17.log NA
## D.npnct19.log D.npnct19.log NA
## D.npnct20.log D.npnct20.log NA
## D.npnct21.log D.npnct21.log NA
## D.npnct22.log D.npnct22.log NA
## D.npnct23.log D.npnct23.log NA
## D.npnct25.log D.npnct25.log NA
## D.npnct26.log D.npnct26.log NA
## D.npnct27.log D.npnct27.log NA
## D.npnct29.log D.npnct29.log NA
## D.npnct30.log D.npnct30.log NA
## D.P.http D.P.http NA
## exclude.as.feat cor.y.abs
## sold 1 1.0000000000
## biddable 0 0.5485860292
## startprice.log 0 0.4668597069
## startprice 1 0.4556701005
## UniqueID 1 0.1904241613
## condition.fctr 0 0.1531007887
## D.npnct05.log 0 0.1178427734
## prdline.my.fctr 0 0.0871560229
## D.npnct14.log 0 0.0784119354
## cellular.fctr 0 0.0722383245
## carrier.fctr 0 0.0677807650
## D.ndgts.log 0 0.0633901377
## D.nwrds.unq.log 0 0.0619524203
## D.npnct09.log 0 0.0617301891
## D.nwrds.log 0 0.0587159086
## D.nchrs.log 0 0.0565815056
## D.ratio.nstopwrds.nwrds 0 0.0554866401
## D.nuppr.log 0 0.0554742013
## D.npnct12.log 0 0.0549329137
## D.T.like 0 0.0533938586
## D.npnct28.log 0 0.0523776347
## D.T.box 0 0.0515596986
## D.npnct06.log 0 0.0497583609
## D.T.this 0 0.0477867670
## D.npnct24.log 0 0.0459503420
## D.npnct16.log 0 0.0447153098
## D.nstopwrds.log 0 0.0439279746
## color.fctr 0 0.0426291775
## D.T.condit 0 0.0415362400
## D.npnct15.log 0 0.0400921803
## D.npnct08.log 0 0.0394718187
## D.npnct13.log 0 0.0349433657
## D.T.screen 0 0.0312904991
## D.T.new 0 0.0302966020
## D.sum.TfIdf 0 0.0300344031
## D.npnct03.log 0 0.0258721719
## D.T.used 0 0.0251046087
## D.npnct10.log 0 0.0240301079
## D.T.ipad 0 0.0234288421
## .rnorm 0 0.0228561937
## D.npnct18.log 0 0.0214919447
## D.T.excellent 0 0.0203632794
## D.npnct11.log 0 0.0188179512
## D.T.great 0 0.0171206415
## D.ratio.sum.TfIdf.nwrds 0 0.0119528209
## D.T.condition 0 0.0118174892
## D.P.mini 0 0.0111321924
## storage.fctr 0 0.0103459049
## D.T.use 0 0.0097129508
## D.P.air 0 0.0091681483
## D.T.scratch 0 0.0091382072
## D.T.good 0 0.0063426895
## D.npnct01.log 0 0.0042941114
## D.T.work 0 0.0035586740
## D.T.veri 0 0.0001659508
## D.npnct02.log 0 NA
## D.npnct04.log 0 NA
## D.npnct07.log 0 NA
## D.npnct17.log 0 NA
## D.npnct19.log 0 NA
## D.npnct20.log 0 NA
## D.npnct21.log 0 NA
## D.npnct22.log 0 NA
## D.npnct23.log 0 NA
## D.npnct25.log 0 NA
## D.npnct26.log 0 NA
## D.npnct27.log 0 NA
## D.npnct29.log 0 NA
## D.npnct30.log 0 NA
## D.P.http 0 NA# sav_feats_df <- glb_feats_df; glb_feats_df <- sav_feats_df
print(glb_feats_df <- orderBy(~-cor.y,
myfind_cor_features(feats_df=glb_feats_df, obs_df=glb_trnobs_df,
rsp_var=glb_rsp_var)))## [1] "cor(D.nchrs.log, D.nuppr.log)=0.9995"
## [1] "cor(sold.fctr, D.nchrs.log)=-0.0566"
## [1] "cor(sold.fctr, D.nuppr.log)=-0.0555"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nuppr.log as highly correlated with
## D.nchrs.log## [1] "cor(D.nchrs.log, D.nwrds.log)=0.9931"
## [1] "cor(sold.fctr, D.nchrs.log)=-0.0566"
## [1] "cor(sold.fctr, D.nwrds.log)=-0.0587"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nchrs.log as highly correlated with
## D.nwrds.log## [1] "cor(D.nwrds.log, D.nwrds.unq.log)=0.9892"
## [1] "cor(sold.fctr, D.nwrds.log)=-0.0587"
## [1] "cor(sold.fctr, D.nwrds.unq.log)=-0.0620"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nwrds.log as highly correlated with
## D.nwrds.unq.log## [1] "cor(D.npnct24.log, D.ratio.nstopwrds.nwrds)=-0.9641"
## [1] "cor(sold.fctr, D.npnct24.log)=-0.0460"
## [1] "cor(sold.fctr, D.ratio.nstopwrds.nwrds)=0.0555"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.npnct24.log as highly correlated with
## D.ratio.nstopwrds.nwrds## [1] "cor(D.npnct06.log, D.npnct16.log)=0.9556"
## [1] "cor(sold.fctr, D.npnct06.log)=-0.0498"
## [1] "cor(sold.fctr, D.npnct16.log)=-0.0447"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.npnct16.log as highly correlated with
## D.npnct06.log## [1] "cor(D.nwrds.unq.log, D.sum.TfIdf)=0.9437"
## [1] "cor(sold.fctr, D.nwrds.unq.log)=-0.0620"
## [1] "cor(sold.fctr, D.sum.TfIdf)=-0.0300"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.sum.TfIdf as highly correlated with
## D.nwrds.unq.log## [1] "cor(D.nwrds.unq.log, D.ratio.nstopwrds.nwrds)=-0.9223"
## [1] "cor(sold.fctr, D.nwrds.unq.log)=-0.0620"
## [1] "cor(sold.fctr, D.ratio.nstopwrds.nwrds)=0.0555"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.ratio.nstopwrds.nwrds as highly correlated
## with D.nwrds.unq.log## [1] "cor(D.nstopwrds.log, D.nwrds.unq.log)=0.8970"
## [1] "cor(sold.fctr, D.nstopwrds.log)=-0.0439"
## [1] "cor(sold.fctr, D.nwrds.unq.log)=-0.0620"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nstopwrds.log as highly correlated with
## D.nwrds.unq.log## [1] "cor(carrier.fctr, cellular.fctr)=0.8462"
## [1] "cor(sold.fctr, carrier.fctr)=-0.0678"
## [1] "cor(sold.fctr, cellular.fctr)=-0.0722"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified carrier.fctr as highly correlated with
## cellular.fctr## [1] "cor(D.npnct13.log, D.nwrds.unq.log)=0.7214"
## [1] "cor(sold.fctr, D.npnct13.log)=-0.0349"
## [1] "cor(sold.fctr, D.nwrds.unq.log)=-0.0620"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.npnct13.log as highly correlated with
## D.nwrds.unq.log## [1] "cor(D.T.like, D.T.new)=0.7047"
## [1] "cor(sold.fctr, D.T.like)=-0.0534"
## [1] "cor(sold.fctr, D.T.new)=-0.0303"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.T.new as highly correlated with D.T.like## id cor.y exclude.as.feat cor.y.abs
## 66 sold 1.0000000000 1 1.0000000000
## 2 biddable 0.5485860292 0 0.5485860292
## 46 D.ratio.nstopwrds.nwrds 0.0554866401 0 0.0554866401
## 23 D.npnct15.log 0.0400921803 0 0.0400921803
## 59 D.T.screen 0.0312904991 0 0.0312904991
## 11 D.npnct03.log 0.0258721719 0 0.0258721719
## 62 D.T.used 0.0251046087 0 0.0251046087
## 52 D.T.excellent 0.0203632794 0 0.0203632794
## 54 D.T.great 0.0171206415 0 0.0171206415
## 47 D.ratio.sum.TfIdf.nwrds 0.0119528209 0 0.0119528209
## 53 D.T.good 0.0063426895 0 0.0063426895
## 9 D.npnct01.log 0.0042941114 0 0.0042941114
## 63 D.T.veri -0.0001659508 0 0.0001659508
## 64 D.T.work -0.0035586740 0 0.0035586740
## 58 D.T.scratch -0.0091382072 0 0.0091382072
## 43 D.P.air -0.0091681483 0 0.0091681483
## 61 D.T.use -0.0097129508 0 0.0097129508
## 69 storage.fctr -0.0103459049 0 0.0103459049
## 45 D.P.mini -0.0111321924 0 0.0111321924
## 51 D.T.condition -0.0118174892 0 0.0118174892
## 19 D.npnct11.log -0.0188179512 0 0.0188179512
## 26 D.npnct18.log -0.0214919447 0 0.0214919447
## 1 .rnorm -0.0228561937 0 0.0228561937
## 55 D.T.ipad -0.0234288421 0 0.0234288421
## 18 D.npnct10.log -0.0240301079 0 0.0240301079
## 48 D.sum.TfIdf -0.0300344031 0 0.0300344031
## 57 D.T.new -0.0302966020 0 0.0302966020
## 21 D.npnct13.log -0.0349433657 0 0.0349433657
## 16 D.npnct08.log -0.0394718187 0 0.0394718187
## 50 D.T.condit -0.0415362400 0 0.0415362400
## 5 color.fctr -0.0426291775 0 0.0426291775
## 39 D.nstopwrds.log -0.0439279746 0 0.0439279746
## 24 D.npnct16.log -0.0447153098 0 0.0447153098
## 32 D.npnct24.log -0.0459503420 0 0.0459503420
## 60 D.T.this -0.0477867670 0 0.0477867670
## 14 D.npnct06.log -0.0497583609 0 0.0497583609
## 49 D.T.box -0.0515596986 0 0.0515596986
## 36 D.npnct28.log -0.0523776347 0 0.0523776347
## 56 D.T.like -0.0533938586 0 0.0533938586
## 20 D.npnct12.log -0.0549329137 0 0.0549329137
## 40 D.nuppr.log -0.0554742013 0 0.0554742013
## 7 D.nchrs.log -0.0565815056 0 0.0565815056
## 41 D.nwrds.log -0.0587159086 0 0.0587159086
## 17 D.npnct09.log -0.0617301891 0 0.0617301891
## 42 D.nwrds.unq.log -0.0619524203 0 0.0619524203
## 8 D.ndgts.log -0.0633901377 0 0.0633901377
## 3 carrier.fctr -0.0677807650 0 0.0677807650
## 4 cellular.fctr -0.0722383245 0 0.0722383245
## 22 D.npnct14.log -0.0784119354 0 0.0784119354
## 65 prdline.my.fctr -0.0871560229 0 0.0871560229
## 13 D.npnct05.log -0.1178427734 0 0.1178427734
## 6 condition.fctr -0.1531007887 0 0.1531007887
## 70 UniqueID -0.1904241613 1 0.1904241613
## 67 startprice -0.4556701005 1 0.4556701005
## 68 startprice.log -0.4668597069 0 0.4668597069
## 10 D.npnct02.log NA 0 NA
## 12 D.npnct04.log NA 0 NA
## 15 D.npnct07.log NA 0 NA
## 25 D.npnct17.log NA 0 NA
## 27 D.npnct19.log NA 0 NA
## 28 D.npnct20.log NA 0 NA
## 29 D.npnct21.log NA 0 NA
## 30 D.npnct22.log NA 0 NA
## 31 D.npnct23.log NA 0 NA
## 33 D.npnct25.log NA 0 NA
## 34 D.npnct26.log NA 0 NA
## 35 D.npnct27.log NA 0 NA
## 37 D.npnct29.log NA 0 NA
## 38 D.npnct30.log NA 0 NA
## 44 D.P.http NA 0 NA
## cor.high.X freqRatio percentUnique zeroVar nzv
## 66 <NA> 1.163953 0.10746910 FALSE FALSE
## 2 <NA> 1.223417 0.10746910 FALSE FALSE
## 46 D.nwrds.unq.log 13.387500 4.67490596 FALSE FALSE
## 23 <NA> 153.500000 0.16120365 FALSE TRUE
## 59 <NA> 45.105263 0.80601827 FALSE TRUE
## 11 <NA> 83.318182 0.16120365 FALSE TRUE
## 62 <NA> 161.818182 0.85975282 FALSE TRUE
## 52 <NA> 227.875000 0.80601827 FALSE TRUE
## 54 <NA> 127.214286 0.75228372 FALSE TRUE
## 47 <NA> 63.294118 35.14239656 FALSE FALSE
## 53 <NA> 39.181818 1.02095648 FALSE TRUE
## 9 <NA> 53.029412 0.32240731 FALSE TRUE
## 63 <NA> 163.090909 0.85975282 FALSE TRUE
## 64 <NA> 82.761905 0.91348737 FALSE TRUE
## 58 <NA> 35.416667 0.80601827 FALSE TRUE
## 43 <NA> 123.000000 0.16120365 FALSE TRUE
## 61 <NA> 57.533333 0.96722192 FALSE TRUE
## 69 <NA> 2.739003 0.26867276 FALSE FALSE
## 45 <NA> 92.000000 0.16120365 FALSE TRUE
## 51 <NA> 52.375000 0.80601827 FALSE TRUE
## 19 <NA> 9.385965 0.37614186 FALSE FALSE
## 26 <NA> 1860.000000 0.10746910 FALSE TRUE
## 1 <NA> 1.000000 100.00000000 FALSE FALSE
## 55 <NA> 51.484848 0.91348737 FALSE TRUE
## 18 <NA> 309.000000 0.16120365 FALSE TRUE
## 48 D.nwrds.unq.log 63.294118 34.87372380 FALSE FALSE
## 57 D.T.like 147.166667 0.91348737 FALSE TRUE
## 21 D.nwrds.unq.log 5.210728 0.48361096 FALSE FALSE
## 16 <NA> 69.653846 0.21493821 FALSE TRUE
## 50 <NA> 52.000000 0.85975282 FALSE TRUE
## 5 <NA> 1.576837 0.26867276 FALSE FALSE
## 39 D.nwrds.unq.log 14.144578 0.80601827 FALSE FALSE
## 24 D.npnct06.log 31.280702 0.16120365 FALSE TRUE
## 32 D.ratio.nstopwrds.nwrds 1.355696 0.10746910 FALSE FALSE
## 60 <NA> 40.744186 0.69854917 FALSE TRUE
## 14 <NA> 33.773585 0.16120365 FALSE TRUE
## 49 <NA> 111.875000 0.85975282 FALSE TRUE
## 36 <NA> 463.750000 0.16120365 FALSE TRUE
## 56 <NA> 259.142857 0.85975282 FALSE TRUE
## 20 <NA> 27.261538 0.21493821 FALSE TRUE
## 40 D.nchrs.log 16.507692 4.35249866 FALSE FALSE
## 7 D.nwrds.log 14.875000 5.64212789 FALSE FALSE
## 41 D.nwrds.unq.log 12.310345 1.39709833 FALSE FALSE
## 17 <NA> 308.666667 0.21493821 FALSE TRUE
## 42 <NA> 7.685714 0.91348737 FALSE FALSE
## 8 <NA> 27.063492 0.69854917 FALSE TRUE
## 3 cellular.fctr 3.192529 0.37614186 FALSE FALSE
## 4 <NA> 2.120229 0.16120365 FALSE FALSE
## 22 <NA> 35.372549 0.26867276 FALSE TRUE
## 65 <NA> 1.135048 0.37614186 FALSE FALSE
## 13 <NA> 40.355556 0.10746910 FALSE TRUE
## 6 <NA> 4.006920 0.32240731 FALSE FALSE
## 70 <NA> 1.000000 100.00000000 FALSE FALSE
## 67 <NA> 2.807692 30.14508329 FALSE FALSE
## 68 <NA> 2.807692 30.14508329 FALSE FALSE
## 10 <NA> 0.000000 0.05373455 TRUE TRUE
## 12 <NA> 0.000000 0.05373455 TRUE TRUE
## 15 <NA> 0.000000 0.05373455 TRUE TRUE
## 25 <NA> 0.000000 0.05373455 TRUE TRUE
## 27 <NA> 0.000000 0.05373455 TRUE TRUE
## 28 <NA> 0.000000 0.05373455 TRUE TRUE
## 29 <NA> 0.000000 0.05373455 TRUE TRUE
## 30 <NA> 0.000000 0.05373455 TRUE TRUE
## 31 <NA> 0.000000 0.05373455 TRUE TRUE
## 33 <NA> 0.000000 0.05373455 TRUE TRUE
## 34 <NA> 0.000000 0.05373455 TRUE TRUE
## 35 <NA> 0.000000 0.05373455 TRUE TRUE
## 37 <NA> 0.000000 0.05373455 TRUE TRUE
## 38 <NA> 0.000000 0.05373455 TRUE TRUE
## 44 <NA> 0.000000 0.05373455 TRUE TRUE
## myNearZV is.cor.y.abs.low
## 66 FALSE FALSE
## 2 FALSE FALSE
## 46 FALSE FALSE
## 23 FALSE FALSE
## 59 FALSE FALSE
## 11 FALSE FALSE
## 62 FALSE FALSE
## 52 FALSE TRUE
## 54 FALSE TRUE
## 47 FALSE TRUE
## 53 FALSE TRUE
## 9 FALSE TRUE
## 63 FALSE TRUE
## 64 FALSE TRUE
## 58 FALSE TRUE
## 43 FALSE TRUE
## 61 FALSE TRUE
## 69 FALSE TRUE
## 45 FALSE TRUE
## 51 FALSE TRUE
## 19 FALSE TRUE
## 26 TRUE TRUE
## 1 FALSE FALSE
## 55 FALSE FALSE
## 18 FALSE FALSE
## 48 FALSE FALSE
## 57 FALSE FALSE
## 21 FALSE FALSE
## 16 FALSE FALSE
## 50 FALSE FALSE
## 5 FALSE FALSE
## 39 FALSE FALSE
## 24 FALSE FALSE
## 32 FALSE FALSE
## 60 FALSE FALSE
## 14 FALSE FALSE
## 49 FALSE FALSE
## 36 FALSE FALSE
## 56 FALSE FALSE
## 20 FALSE FALSE
## 40 FALSE FALSE
## 7 FALSE FALSE
## 41 FALSE FALSE
## 17 FALSE FALSE
## 42 FALSE FALSE
## 8 FALSE FALSE
## 3 FALSE FALSE
## 4 FALSE FALSE
## 22 FALSE FALSE
## 65 FALSE FALSE
## 13 FALSE FALSE
## 6 FALSE FALSE
## 70 FALSE FALSE
## 67 FALSE FALSE
## 68 FALSE FALSE
## 10 TRUE NA
## 12 TRUE NA
## 15 TRUE NA
## 25 TRUE NA
## 27 TRUE NA
## 28 TRUE NA
## 29 TRUE NA
## 30 TRUE NA
## 31 TRUE NA
## 33 TRUE NA
## 34 TRUE NA
## 35 TRUE NA
## 37 TRUE NA
## 38 TRUE NA
## 44 TRUE NA#subset(glb_feats_df, id %in% c("A.nuppr.log", "S.nuppr.log"))
print(myplot_scatter(glb_feats_df, "percentUnique", "freqRatio",
colorcol_name="myNearZV", jitter=TRUE) +
geom_point(aes(shape=nzv)) + xlim(-5, 25))## Warning in myplot_scatter(glb_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "myNearZV", : converting myNearZV to class:factor## Warning: Removed 6 rows containing missing values (geom_point).## Warning: Removed 6 rows containing missing values (geom_point).## Warning: Removed 6 rows containing missing values (geom_point).
print(subset(glb_feats_df, myNearZV))## id cor.y exclude.as.feat cor.y.abs cor.high.X
## 26 D.npnct18.log -0.02149194 0 0.02149194 <NA>
## 10 D.npnct02.log NA 0 NA <NA>
## 12 D.npnct04.log NA 0 NA <NA>
## 15 D.npnct07.log NA 0 NA <NA>
## 25 D.npnct17.log NA 0 NA <NA>
## 27 D.npnct19.log NA 0 NA <NA>
## 28 D.npnct20.log NA 0 NA <NA>
## 29 D.npnct21.log NA 0 NA <NA>
## 30 D.npnct22.log NA 0 NA <NA>
## 31 D.npnct23.log NA 0 NA <NA>
## 33 D.npnct25.log NA 0 NA <NA>
## 34 D.npnct26.log NA 0 NA <NA>
## 35 D.npnct27.log NA 0 NA <NA>
## 37 D.npnct29.log NA 0 NA <NA>
## 38 D.npnct30.log NA 0 NA <NA>
## 44 D.P.http NA 0 NA <NA>
## freqRatio percentUnique zeroVar nzv myNearZV is.cor.y.abs.low
## 26 1860 0.10746910 FALSE TRUE TRUE TRUE
## 10 0 0.05373455 TRUE TRUE TRUE NA
## 12 0 0.05373455 TRUE TRUE TRUE NA
## 15 0 0.05373455 TRUE TRUE TRUE NA
## 25 0 0.05373455 TRUE TRUE TRUE NA
## 27 0 0.05373455 TRUE TRUE TRUE NA
## 28 0 0.05373455 TRUE TRUE TRUE NA
## 29 0 0.05373455 TRUE TRUE TRUE NA
## 30 0 0.05373455 TRUE TRUE TRUE NA
## 31 0 0.05373455 TRUE TRUE TRUE NA
## 33 0 0.05373455 TRUE TRUE TRUE NA
## 34 0 0.05373455 TRUE TRUE TRUE NA
## 35 0 0.05373455 TRUE TRUE TRUE NA
## 37 0 0.05373455 TRUE TRUE TRUE NA
## 38 0 0.05373455 TRUE TRUE TRUE NA
## 44 0 0.05373455 TRUE TRUE TRUE NAglb_allobs_df <- glb_allobs_df[, setdiff(names(glb_allobs_df),
subset(glb_feats_df, myNearZV)$id)]
glb_trnobs_df <- subset(glb_allobs_df, .src == "Train")
glb_newobs_df <- subset(glb_allobs_df, .src == "Test")
if (!is.null(glb_interaction_only_features))
glb_feats_df[glb_feats_df$id %in% glb_interaction_only_features, "interaction.feat"] <-
names(glb_interaction_only_features) else
glb_feats_df$interaction.feat <- NA
mycheck_problem_data(glb_allobs_df, terminate = TRUE)## [1] "numeric data missing in : "
## sold sold.fctr
## 798 798
## [1] "numeric data w/ 0s in : "
## biddable sold startprice.log
## 1446 1001 31
## cellular.fctr D.T.condit D.T.condition
## 1599 2455 2385
## D.T.good D.T.ipad D.T.new
## 2463 2432 2520
## D.T.scratch D.T.screen D.T.this
## 2431 2453 2503
## D.T.use D.T.work D.T.box
## 2474 2485 2553
## D.T.excellent D.T.great D.T.like
## 2602 2539 2591
## D.T.used D.T.veri D.nwrds.log
## 2540 2567 1521
## D.nwrds.unq.log D.sum.TfIdf D.ratio.sum.TfIdf.nwrds
## 1529 1529 1529
## D.nchrs.log D.nuppr.log D.ndgts.log
## 1521 1523 2428
## D.npnct01.log D.npnct03.log D.npnct05.log
## 2581 2616 2594
## D.npnct06.log D.npnct08.log D.npnct09.log
## 2556 2583 2643
## D.npnct10.log D.npnct11.log D.npnct12.log
## 2650 2303 2539
## D.npnct13.log D.npnct14.log D.npnct15.log
## 1934 2584 2638
## D.npnct16.log D.npnct24.log D.npnct28.log
## 2548 1521 2651
## D.nstopwrds.log D.P.mini D.P.air
## 1669 2625 2639
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1521 0 0 0 0 0
## productline prdline.my
## 0 0# glb_allobs_df %>% filter(is.na(Married.fctr)) %>% tbl_df()
# glb_allobs_df %>% count(Married.fctr)
# levels(glb_allobs_df$Married.fctr)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "partition.data.training", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 8 select.features 5 0 40.217 41.783 1.566
## 9 partition.data.training 6 0 41.784 NA NAStep 6.0: partition data training
if (all(is.na(glb_newobs_df[, glb_rsp_var]))) {
set.seed(glb_split_sample.seed)
OOB_size <- nrow(glb_newobs_df) * 1.1
if (is.null(glb_category_vars)) {
require(caTools)
split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw],
SplitRatio=OOB_size / nrow(glb_trnobs_df))
glb_OOBobs_df <- glb_trnobs_df[split ,]
glb_fitobs_df <- glb_trnobs_df[!split, ]
} else {
sample_vars <- c(glb_rsp_var_raw, glb_category_vars)
rspvar_freq_df <- orderBy(reformulate(glb_rsp_var_raw),
mycreate_sqlxtab_df(glb_trnobs_df, glb_rsp_var_raw))
OOB_rspvar_size <- 1.0 * OOB_size * rspvar_freq_df$.n / sum(rspvar_freq_df$.n)
newobs_freq_df <- orderBy(reformulate(glb_category_vars),
mycreate_sqlxtab_df(glb_newobs_df, glb_category_vars))
trnobs_freq_df <- orderBy(reformulate(glb_category_vars),
mycreate_sqlxtab_df(glb_trnobs_df, glb_category_vars))
allobs_freq_df <- merge(newobs_freq_df, trnobs_freq_df, by=glb_category_vars,
all=TRUE, sort=TRUE, suffixes=c(".Tst", ".Train"))
allobs_freq_df[is.na(allobs_freq_df)] <- 0
OOB_strata_size <- ceiling(
as.vector(matrix(allobs_freq_df$.n.Tst * 1.0 / sum(allobs_freq_df$.n.Tst)) %*%
matrix(OOB_rspvar_size, nrow=1)))
OOB_strata_size[OOB_strata_size == 0] <- 1
OOB_strata_df <- expand.grid(glb_rsp_var_raw=rspvar_freq_df[, glb_rsp_var_raw],
glb_category_vars=allobs_freq_df[, glb_category_vars])
names(OOB_strata_df) <- sample_vars
OOB_strata_df <- orderBy(reformulate(sample_vars), OOB_strata_df)
trnobs_univ_df <- orderBy(reformulate(sample_vars),
mycreate_sqlxtab_df(glb_trnobs_df, sample_vars))
trnobs_univ_df <- merge(trnobs_univ_df, OOB_strata_df, all=TRUE)
tmp_trnobs_df <- orderBy(reformulate(c(glb_rsp_var_raw, glb_category_vars)),
glb_trnobs_df)
require(sampling)
split_strata <- strata(tmp_trnobs_df,
stratanames=c(glb_rsp_var_raw, glb_category_vars),
size=OOB_strata_size[!is.na(trnobs_univ_df$.n)],
method="srswor")
glb_OOBobs_df <- getdata(tmp_trnobs_df, split_strata)[, names(glb_trnobs_df)]
glb_fitobs_df <- glb_trnobs_df[!glb_trnobs_df[, glb_id_var] %in%
glb_OOBobs_df[, glb_id_var], ]
}
} else {
print(sprintf("Newdata contains non-NA data for %s; setting OOB to Newdata",
glb_rsp_var))
glb_fitobs_df <- glb_trnobs_df; glb_OOBobs_df <- glb_newobs_df
}## Loading required package: sampling
##
## Attaching package: 'sampling'
##
## The following objects are masked from 'package:survival':
##
## cluster, strata
##
## The following object is masked from 'package:caret':
##
## clusterif (!is.null(glb_max_fitobs) && (nrow(glb_fitobs_df) > glb_max_fitobs)) {
warning("glb_fitobs_df restricted to glb_max_fitobs: ",
format(glb_max_fitobs, big.mark=","))
org_fitobs_df <- glb_fitobs_df
glb_fitobs_df <-
org_fitobs_df[split <- sample.split(org_fitobs_df[, glb_rsp_var_raw],
SplitRatio=glb_max_fitobs), ]
org_fitobs_df <- NULL
}
glb_allobs_df$.lcn <- ""
glb_allobs_df[glb_allobs_df[, glb_id_var] %in%
glb_fitobs_df[, glb_id_var], ".lcn"] <- "Fit"
glb_allobs_df[glb_allobs_df[, glb_id_var] %in%
glb_OOBobs_df[, glb_id_var], ".lcn"] <- "OOB"
dsp_class_dstrb <- function(obs_df, location_var, partition_var) {
xtab_df <- mycreate_xtab_df(obs_df, c(location_var, partition_var))
rownames(xtab_df) <- xtab_df[, location_var]
xtab_df <- xtab_df[, -grepl(location_var, names(xtab_df))]
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
# Ensure proper splits by glb_rsp_var_raw & user-specified feature for OOB vs. new
if (!is.null(glb_category_vars)) {
if (glb_is_classification)
dsp_class_dstrb(glb_allobs_df, ".lcn", glb_rsp_var_raw)
newobs_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .src == "Test"),
glb_category_vars)
OOBobs_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .lcn == "OOB"),
glb_category_vars)
glb_ctgry_df <- merge(newobs_ctgry_df, OOBobs_ctgry_df, by=glb_category_vars
, all=TRUE, suffixes=c(".Tst", ".OOB"))
glb_ctgry_df$.freqRatio.Tst <- glb_ctgry_df$.n.Tst / sum(glb_ctgry_df$.n.Tst, na.rm=TRUE)
glb_ctgry_df$.freqRatio.OOB <- glb_ctgry_df$.n.OOB / sum(glb_ctgry_df$.n.OOB, na.rm=TRUE)
print(orderBy(~-.freqRatio.Tst-.freqRatio.OOB, glb_ctgry_df))
}## sold.0 sold.1 sold.NA
## NA NA 798
## Fit 525 451 NA
## OOB 476 409 NA
## sold.0 sold.1 sold.NA
## NA NA 1
## Fit 0.5379098 0.4620902 NA
## OOB 0.5378531 0.4621469 NA
## prdline.my .n.Tst .n.OOB .freqRatio.Tst .freqRatio.OOB
## 2 iPad 2 154 171 0.1929825 0.1932203
## 4 iPadAir 137 152 0.1716792 0.1717514
## 3 iPad 3+ 123 136 0.1541353 0.1536723
## 5 iPadmini 114 126 0.1428571 0.1423729
## 6 iPadmini 2+ 94 104 0.1177945 0.1175141
## 1 iPad 1 89 99 0.1115288 0.1118644
## 7 Unknown 87 97 0.1090226 0.1096045# Run this line by line
print("glb_feats_df:"); print(dim(glb_feats_df))## [1] "glb_feats_df:"## [1] 70 12sav_feats_df <- glb_feats_df
glb_feats_df <- sav_feats_df
glb_feats_df[, "rsp_var_raw"] <- FALSE
glb_feats_df[glb_feats_df$id == glb_rsp_var_raw, "rsp_var_raw"] <- TRUE
glb_feats_df$exclude.as.feat <- (glb_feats_df$exclude.as.feat == 1)
if (!is.null(glb_id_var) && glb_id_var != ".rownames")
glb_feats_df[glb_feats_df$id %in% glb_id_var, "id_var"] <- TRUE
add_feats_df <- data.frame(id=glb_rsp_var, exclude.as.feat=TRUE, rsp_var=TRUE)
row.names(add_feats_df) <- add_feats_df$id; print(add_feats_df)## id exclude.as.feat rsp_var
## sold.fctr sold.fctr TRUE TRUEglb_feats_df <- myrbind_df(glb_feats_df, add_feats_df)
if (glb_id_var != ".rownames")
print(subset(glb_feats_df, rsp_var_raw | rsp_var | id_var)) else
print(subset(glb_feats_df, rsp_var_raw | rsp_var)) ## id cor.y exclude.as.feat cor.y.abs cor.high.X
## 66 sold 1.0000000 TRUE 1.0000000 <NA>
## 70 UniqueID -0.1904242 TRUE 0.1904242 <NA>
## sold.fctr sold.fctr NA TRUE NA <NA>
## freqRatio percentUnique zeroVar nzv myNearZV is.cor.y.abs.low
## 66 1.163953 0.1074691 FALSE FALSE FALSE FALSE
## 70 1.000000 100.0000000 FALSE FALSE FALSE FALSE
## sold.fctr NA NA NA NA NA NA
## interaction.feat rsp_var_raw id_var rsp_var
## 66 NA TRUE NA NA
## 70 NA FALSE TRUE NA
## sold.fctr NA NA NA TRUEprint("glb_feats_df vs. glb_allobs_df: "); ## [1] "glb_feats_df vs. glb_allobs_df: "print(setdiff(glb_feats_df$id, names(glb_allobs_df)))## [1] "D.npnct18.log" "D.npnct02.log" "D.npnct04.log" "D.npnct07.log"
## [5] "D.npnct17.log" "D.npnct19.log" "D.npnct20.log" "D.npnct21.log"
## [9] "D.npnct22.log" "D.npnct23.log" "D.npnct25.log" "D.npnct26.log"
## [13] "D.npnct27.log" "D.npnct29.log" "D.npnct30.log" "D.P.http"print("glb_allobs_df vs. glb_feats_df: "); ## [1] "glb_allobs_df vs. glb_feats_df: "# Ensure these are only chr vars
print(setdiff(setdiff(names(glb_allobs_df), glb_feats_df$id),
myfind_chr_cols_df(glb_allobs_df)))## character(0)#print(setdiff(setdiff(names(glb_allobs_df), glb_exclude_vars_as_features),
# glb_feats_df$id))
print("glb_allobs_df: "); print(dim(glb_allobs_df))## [1] "glb_allobs_df: "## [1] 2659 65print("glb_trnobs_df: "); print(dim(glb_trnobs_df))## [1] "glb_trnobs_df: "## [1] 1861 64print("glb_fitobs_df: "); print(dim(glb_fitobs_df))## [1] "glb_fitobs_df: "## [1] 976 64print("glb_OOBobs_df: "); print(dim(glb_OOBobs_df))## [1] "glb_OOBobs_df: "## [1] 885 64print("glb_newobs_df: "); print(dim(glb_newobs_df))## [1] "glb_newobs_df: "## [1] 798 64# # Does not handle NULL or length(glb_id_var) > 1
# glb_allobs_df$.src.trn <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_trnobs_df[, glb_id_var],
# ".src.trn"] <- 1
# glb_allobs_df$.src.fit <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_fitobs_df[, glb_id_var],
# ".src.fit"] <- 1
# glb_allobs_df$.src.OOB <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_OOBobs_df[, glb_id_var],
# ".src.OOB"] <- 1
# glb_allobs_df$.src.new <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_newobs_df[, glb_id_var],
# ".src.new"] <- 1
# #print(unique(glb_allobs_df[, ".src.trn"]))
# write_cols <- c(glb_feats_df$id,
# ".src.trn", ".src.fit", ".src.OOB", ".src.new")
# glb_allobs_df <- glb_allobs_df[, write_cols]
#
# tmp_feats_df <- glb_feats_df
# tmp_entity_df <- glb_allobs_df
if (glb_save_envir)
save(glb_feats_df,
glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
file=paste0(glb_out_pfx, "blddfs_dsk.RData"))
# load(paste0(glb_out_pfx, "blddfs_dsk.RData"))
# if (!all.equal(tmp_feats_df, glb_feats_df))
# stop("glb_feats_df r/w not working")
# if (!all.equal(tmp_entity_df, glb_allobs_df))
# stop("glb_allobs_df r/w not working")
rm(split)## Warning in rm(split): object 'split' not foundglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 9 partition.data.training 6 0 41.784 42.675 0.891
## 10 fit.models 7 0 42.675 NA NAStep 7.0: fit models
# load(paste0(glb_out_pfx, "dsk.RData"))
# keep_cols <- setdiff(names(glb_allobs_df),
# grep("^.src", names(glb_allobs_df), value=TRUE))
# glb_trnobs_df <- glb_allobs_df[glb_allobs_df$.src.trn == 1, keep_cols]
# glb_fitobs_df <- glb_allobs_df[glb_allobs_df$.src.fit == 1, keep_cols]
# glb_OOBobs_df <- glb_allobs_df[glb_allobs_df$.src.OOB == 1, keep_cols]
# glb_newobs_df <- glb_allobs_df[glb_allobs_df$.src.new == 1, keep_cols]
#
# glb_models_lst <- list(); glb_models_df <- data.frame()
#
if (glb_is_classification && glb_is_binomial &&
(length(unique(glb_fitobs_df[, glb_rsp_var])) < 2))
stop("glb_fitobs_df$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glb_fitobs_df[, glb_rsp_var]), collapse=", "))
max_cor_y_x_vars <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !is.cor.y.abs.low &
is.na(cor.high.X)))[1:2, "id"]
# while(length(max_cor_y_x_vars) < 2) {
# max_cor_y_x_vars <- c(max_cor_y_x_vars, orderBy(~ -cor.y.abs,
# subset(glb_feats_df, (exclude.as.feat == 0) & !is.cor.y.abs.low))[3, "id"])
# }
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) &
(glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] >
glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Baseline
if (!is.null(glb_Baseline_mdl_var))
ret_lst <- myfit_mdl(model_id="Baseline",
model_method="mybaseln_classfr",
indep_vars_vctr=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
ret_lst <- myfit_mdl(model_id="MFO",
model_method=ifelse(glb_is_regression, "lm", "myMFO_classfr"),
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: MFO.myMFO_classfr"
## [1] " indep_vars: .rnorm"
## Fitting parameter = none on full training set
## [1] "in MFO.Classifier$fit"
## [1] "unique.vals:"
## [1] N Y
## Levels: N Y
## [1] "unique.prob:"
## y
## N Y
## 0.5379098 0.4620902
## [1] "MFO.val:"
## [1] "N"
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 -none- numeric
## MFO.val 1 -none- character
## x.names 1 -none- character
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] " calling mypredict_mdl for fit:"## Loading required package: ROCR
## Loading required package: gplots
##
## Attaching package: 'gplots'
##
## The following object is masked from 'package:stats':
##
## lowess## [1] "in MFO.Classifier$prob"
## N Y
## 1 0.5379098 0.4620902
## 2 0.5379098 0.4620902
## 3 0.5379098 0.4620902
## 4 0.5379098 0.4620902
## 5 0.5379098 0.4620902
## 6 0.5379098 0.4620902
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.MFO.myMFO_classfr.N
## 1 N 525
## 2 Y 451
## Prediction
## Reference N Y
## N 525 0
## Y 451 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.379098e-01 0.000000e+00 5.060449e-01 5.695455e-01 5.379098e-01
## AccuracyPValue McnemarPValue
## 5.131267e-01 1.189217e-99
## [1] " calling mypredict_mdl for OOB:"
## [1] "in MFO.Classifier$prob"
## N Y
## 1 0.5379098 0.4620902
## 2 0.5379098 0.4620902
## 3 0.5379098 0.4620902
## 4 0.5379098 0.4620902
## 5 0.5379098 0.4620902
## 6 0.5379098 0.4620902
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.MFO.myMFO_classfr.N
## 1 N 476
## 2 Y 409
## Prediction
## Reference N Y
## N 476 0
## Y 409 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.378531e-01 0.000000e+00 5.043611e-01 5.710923e-01 5.378531e-01
## AccuracyPValue McnemarPValue
## 5.137840e-01 1.646732e-90
## model_id model_method feats max.nTuningRuns
## 1 MFO.myMFO_classfr myMFO_classfr .rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.366 0.002 0.5
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0 0.5379098
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.5060449 0.5695455 0 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0 0.5378531
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5043611 0.5710923 0if (glb_is_classification)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
ret_lst <- myfit_mdl(model_id="Random", model_method="myrandom_classfr",
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Random.myrandom_classfr"
## [1] " indep_vars: .rnorm"
## Fitting parameter = none on full training set
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 table numeric
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] " calling mypredict_mdl for fit:"
## [1] "in Random.Classifier$prob"
## threshold f.score
## 1 0.0 0.6320953
## 2 0.1 0.6320953
## 3 0.2 0.6320953
## 4 0.3 0.6320953
## 5 0.4 0.6320953
## 6 0.5 0.4714912
## 7 0.6 0.0000000
## 8 0.7 0.0000000
## 9 0.8 0.0000000
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Random.myrandom_classfr.Y
## 1 N 525
## 2 Y 451
## Prediction
## Reference N Y
## N 0 525
## Y 0 451
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 4.620902e-01 0.000000e+00 4.304545e-01 4.939551e-01 5.379098e-01
## AccuracyPValue McnemarPValue
## 9.999991e-01 9.406788e-116
## [1] " calling mypredict_mdl for OOB:"
## [1] "in Random.Classifier$prob"
## threshold f.score
## 1 0.0 0.6321484
## 2 0.1 0.6321484
## 3 0.2 0.6321484
## 4 0.3 0.6321484
## 5 0.4 0.6321484
## 6 0.5 0.4847001
## 7 0.6 0.0000000
## 8 0.7 0.0000000
## 9 0.8 0.0000000
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Random.myrandom_classfr.Y
## 1 N 476
## 2 Y 409
## Prediction
## Reference N Y
## N 0 476
## Y 0 409
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 4.621469e-01 0.000000e+00 4.289077e-01 4.956389e-01 5.378531e-01
## AccuracyPValue McnemarPValue
## 9.999972e-01 4.314099e-105
## model_id model_method feats max.nTuningRuns
## 1 Random.myrandom_classfr myrandom_classfr .rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.253 0.001 0.5040735
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.6320953 0.4620902
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.4304545 0.4939551 0 0.5214656
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.6321484 0.4621469
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.4289077 0.4956389 0# Any models that have tuning parameters has "better" results with cross-validation
# (except rf) & "different" results for different outcome metrics
# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
ret_lst <- myfit_mdl(model_id="Max.cor.Y.cv.0",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Max.cor.Y.cv.0.rpart"
## [1] " indep_vars: biddable, startprice.log"## Loading required package: rpart## Fitting cp = 0.494 on full training set## Loading required package: rpart.plot
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 976
##
## CP nsplit rel error
## 1 0.4944568 0 1
##
## Node number 1: 976 observations
## predicted class=N expected loss=0.4620902 P(node) =1
## class counts: 525 451
## probabilities: 0.538 0.462
##
## n= 976
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 976 451 N (0.5379098 0.4620902) *
## [1] " calling mypredict_mdl for fit:"
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Max.cor.Y.cv.0.rpart.N
## 1 N 525
## 2 Y 451
## Prediction
## Reference N Y
## N 525 0
## Y 451 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.379098e-01 0.000000e+00 5.060449e-01 5.695455e-01 5.379098e-01
## AccuracyPValue McnemarPValue
## 5.131267e-01 1.189217e-99
## [1] " calling mypredict_mdl for OOB:"
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Max.cor.Y.cv.0.rpart.N
## 1 N 476
## 2 Y 409
## Prediction
## Reference N Y
## N 476 0
## Y 409 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.378531e-01 0.000000e+00 5.043611e-01 5.710923e-01 5.378531e-01
## AccuracyPValue McnemarPValue
## 5.137840e-01 1.646732e-90
## model_id model_method feats
## 1 Max.cor.Y.cv.0.rpart rpart biddable, startprice.log
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 0.678 0.011
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.5 0 0.5379098
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.5060449 0.5695455 0 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0 0.5378531
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5043611 0.5710923 0ret_lst <- myfit_mdl(model_id="Max.cor.Y.cv.0.cp.0",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=0,
tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))## [1] "fitting model: Max.cor.Y.cv.0.cp.0.rpart"
## [1] " indep_vars: biddable, startprice.log"
## Fitting cp = 0 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 976
##
## CP nsplit rel error
## 1 0.4944567627 0 1.0000000
## 2 0.0155210643 1 0.5055432
## 3 0.0133037694 3 0.4745011
## 4 0.0110864745 4 0.4611973
## 5 0.0051736881 5 0.4501109
## 6 0.0044345898 8 0.4345898
## 7 0.0036954915 9 0.4301552
## 8 0.0022172949 12 0.4190687
## 9 0.0002217295 20 0.3991131
## 10 0.0000000000 30 0.3968958
##
## Variable importance
## startprice.log biddable
## 51 49
##
## Node number 1: 976 observations, complexity param=0.4944568
## predicted class=N expected loss=0.4620902 P(node) =1
## class counts: 525 451
## probabilities: 0.538 0.462
## left son=2 (539 obs) right son=3 (437 obs)
## Primary splits:
## biddable < 0.5 to the left, improve=135.9194, (0 missing)
## startprice.log < 5.02015 to the right, improve=117.2326, (0 missing)
## Surrogate splits:
## startprice.log < 4.628135 to the right, agree=0.747, adj=0.435, (0 split)
##
## Node number 2: 539 observations, complexity param=0.005173688
## predicted class=N expected loss=0.2244898 P(node) =0.5522541
## class counts: 418 121
## probabilities: 0.776 0.224
## left son=4 (378 obs) right son=5 (161 obs)
## Primary splits:
## startprice.log < 5.220194 to the right, improve=10.0818, (0 missing)
##
## Node number 3: 437 observations, complexity param=0.01552106
## predicted class=Y expected loss=0.2448513 P(node) =0.4477459
## class counts: 107 330
## probabilities: 0.245 0.755
## left son=6 (159 obs) right son=7 (278 obs)
## Primary splits:
## startprice.log < 5.010402 to the right, improve=33.34958, (0 missing)
##
## Node number 4: 378 observations, complexity param=0.0002217295
## predicted class=N expected loss=0.1613757 P(node) =0.3872951
## class counts: 317 61
## probabilities: 0.839 0.161
## left son=8 (273 obs) right son=9 (105 obs)
## Primary splits:
## startprice.log < 5.527188 to the right, improve=1.711437, (0 missing)
##
## Node number 5: 161 observations, complexity param=0.005173688
## predicted class=N expected loss=0.3726708 P(node) =0.164959
## class counts: 101 60
## probabilities: 0.627 0.373
## left son=10 (90 obs) right son=11 (71 obs)
## Primary splits:
## startprice.log < 4.867496 to the left, improve=1.038815, (0 missing)
##
## Node number 6: 159 observations, complexity param=0.01552106
## predicted class=N expected loss=0.4968553 P(node) =0.1629098
## class counts: 80 79
## probabilities: 0.503 0.497
## left son=12 (20 obs) right son=13 (139 obs)
## Primary splits:
## startprice.log < 6.024113 to the right, improve=5.504769, (0 missing)
##
## Node number 7: 278 observations
## predicted class=Y expected loss=0.0971223 P(node) =0.2848361
## class counts: 27 251
## probabilities: 0.097 0.903
##
## Node number 8: 273 observations, complexity param=0.0002217295
## predicted class=N expected loss=0.1318681 P(node) =0.2797131
## class counts: 237 36
## probabilities: 0.868 0.132
## left son=16 (24 obs) right son=17 (249 obs)
## Primary splits:
## startprice.log < 5.634772 to the left, improve=0.9151331, (0 missing)
##
## Node number 9: 105 observations
## predicted class=N expected loss=0.2380952 P(node) =0.107582
## class counts: 80 25
## probabilities: 0.762 0.238
##
## Node number 10: 90 observations, complexity param=0.003695492
## predicted class=N expected loss=0.3222222 P(node) =0.09221311
## class counts: 61 29
## probabilities: 0.678 0.322
## left son=20 (35 obs) right son=21 (55 obs)
## Primary splits:
## startprice.log < 4.574498 to the right, improve=2.604618, (0 missing)
##
## Node number 11: 71 observations, complexity param=0.005173688
## predicted class=N expected loss=0.4366197 P(node) =0.0727459
## class counts: 40 31
## probabilities: 0.563 0.437
## left son=22 (40 obs) right son=23 (31 obs)
## Primary splits:
## startprice.log < 5.026653 to the right, improve=3.4199, (0 missing)
##
## Node number 12: 20 observations
## predicted class=N expected loss=0.15 P(node) =0.0204918
## class counts: 17 3
## probabilities: 0.850 0.150
##
## Node number 13: 139 observations, complexity param=0.01330377
## predicted class=Y expected loss=0.4532374 P(node) =0.142418
## class counts: 63 76
## probabilities: 0.453 0.547
## left son=26 (72 obs) right son=27 (67 obs)
## Primary splits:
## startprice.log < 5.327663 to the right, improve=2.336116, (0 missing)
##
## Node number 16: 24 observations
## predicted class=N expected loss=0 P(node) =0.02459016
## class counts: 24 0
## probabilities: 1.000 0.000
##
## Node number 17: 249 observations, complexity param=0.0002217295
## predicted class=N expected loss=0.1445783 P(node) =0.255123
## class counts: 213 36
## probabilities: 0.855 0.145
## left son=34 (242 obs) right son=35 (7 obs)
## Primary splits:
## startprice.log < 5.664694 to the right, improve=1.16179, (0 missing)
##
## Node number 20: 35 observations
## predicted class=N expected loss=0.1714286 P(node) =0.03586066
## class counts: 29 6
## probabilities: 0.829 0.171
##
## Node number 21: 55 observations, complexity param=0.003695492
## predicted class=N expected loss=0.4181818 P(node) =0.05635246
## class counts: 32 23
## probabilities: 0.582 0.418
## left son=42 (11 obs) right son=43 (44 obs)
## Primary splits:
## startprice.log < 3.365867 to the left, improve=1.536364, (0 missing)
##
## Node number 22: 40 observations, complexity param=0.002217295
## predicted class=N expected loss=0.3 P(node) =0.04098361
## class counts: 28 12
## probabilities: 0.700 0.300
## left son=44 (31 obs) right son=45 (9 obs)
## Primary splits:
## startprice.log < 5.176086 to the left, improve=1.516846, (0 missing)
##
## Node number 23: 31 observations, complexity param=0.002217295
## predicted class=Y expected loss=0.3870968 P(node) =0.0317623
## class counts: 12 19
## probabilities: 0.387 0.613
## left son=46 (9 obs) right son=47 (22 obs)
## Primary splits:
## startprice.log < 4.966276 to the left, improve=0.7197784, (0 missing)
##
## Node number 26: 72 observations, complexity param=0.01108647
## predicted class=N expected loss=0.4583333 P(node) =0.07377049
## class counts: 39 33
## probabilities: 0.542 0.458
## left son=52 (13 obs) right son=53 (59 obs)
## Primary splits:
## startprice.log < 5.482701 to the left, improve=4.615711, (0 missing)
##
## Node number 27: 67 observations, complexity param=0.002217295
## predicted class=Y expected loss=0.358209 P(node) =0.06864754
## class counts: 24 43
## probabilities: 0.358 0.642
## left son=54 (44 obs) right son=55 (23 obs)
## Primary splits:
## startprice.log < 5.206629 to the left, improve=1.388974, (0 missing)
##
## Node number 34: 242 observations, complexity param=0.0002217295
## predicted class=N expected loss=0.1363636 P(node) =0.2479508
## class counts: 209 33
## probabilities: 0.864 0.136
## left son=68 (26 obs) right son=69 (216 obs)
## Primary splits:
## startprice.log < 6.440933 to the right, improve=0.5584046, (0 missing)
##
## Node number 35: 7 observations
## predicted class=N expected loss=0.4285714 P(node) =0.007172131
## class counts: 4 3
## probabilities: 0.571 0.429
##
## Node number 42: 11 observations
## predicted class=N expected loss=0.1818182 P(node) =0.01127049
## class counts: 9 2
## probabilities: 0.818 0.182
##
## Node number 43: 44 observations, complexity param=0.003695492
## predicted class=N expected loss=0.4772727 P(node) =0.04508197
## class counts: 23 21
## probabilities: 0.523 0.477
## left son=86 (33 obs) right son=87 (11 obs)
## Primary splits:
## startprice.log < 4.094261 to the right, improve=1.833333, (0 missing)
##
## Node number 44: 31 observations
## predicted class=N expected loss=0.2258065 P(node) =0.0317623
## class counts: 24 7
## probabilities: 0.774 0.226
##
## Node number 45: 9 observations
## predicted class=Y expected loss=0.4444444 P(node) =0.009221311
## class counts: 4 5
## probabilities: 0.444 0.556
##
## Node number 46: 9 observations
## predicted class=N expected loss=0.4444444 P(node) =0.009221311
## class counts: 5 4
## probabilities: 0.556 0.444
##
## Node number 47: 22 observations
## predicted class=Y expected loss=0.3181818 P(node) =0.02254098
## class counts: 7 15
## probabilities: 0.318 0.682
##
## Node number 52: 13 observations
## predicted class=N expected loss=0.07692308 P(node) =0.01331967
## class counts: 12 1
## probabilities: 0.923 0.077
##
## Node number 53: 59 observations, complexity param=0.00443459
## predicted class=Y expected loss=0.4576271 P(node) =0.06045082
## class counts: 27 32
## probabilities: 0.458 0.542
## left son=106 (8 obs) right son=107 (51 obs)
## Primary splits:
## startprice.log < 5.959529 to the right, improve=0.5185278, (0 missing)
##
## Node number 54: 44 observations, complexity param=0.002217295
## predicted class=Y expected loss=0.4318182 P(node) =0.04508197
## class counts: 19 25
## probabilities: 0.432 0.568
## left son=108 (8 obs) right son=109 (36 obs)
## Primary splits:
## startprice.log < 5.164643 to the right, improve=0.729798, (0 missing)
##
## Node number 55: 23 observations
## predicted class=Y expected loss=0.2173913 P(node) =0.02356557
## class counts: 5 18
## probabilities: 0.217 0.783
##
## Node number 68: 26 observations
## predicted class=N expected loss=0.03846154 P(node) =0.02663934
## class counts: 25 1
## probabilities: 0.962 0.038
##
## Node number 69: 216 observations, complexity param=0.0002217295
## predicted class=N expected loss=0.1481481 P(node) =0.2213115
## class counts: 184 32
## probabilities: 0.852 0.148
## left son=138 (209 obs) right son=139 (7 obs)
## Primary splits:
## startprice.log < 6.376704 to the left, improve=1.137794, (0 missing)
##
## Node number 86: 33 observations
## predicted class=N expected loss=0.3939394 P(node) =0.03381148
## class counts: 20 13
## probabilities: 0.606 0.394
##
## Node number 87: 11 observations
## predicted class=Y expected loss=0.2727273 P(node) =0.01127049
## class counts: 3 8
## probabilities: 0.273 0.727
##
## Node number 106: 8 observations
## predicted class=N expected loss=0.375 P(node) =0.008196721
## class counts: 5 3
## probabilities: 0.625 0.375
##
## Node number 107: 51 observations, complexity param=0.002217295
## predicted class=Y expected loss=0.4313725 P(node) =0.0522541
## class counts: 22 29
## probabilities: 0.431 0.569
## left son=214 (42 obs) right son=215 (9 obs)
## Primary splits:
## startprice.log < 5.836219 to the left, improve=0.9561158, (0 missing)
##
## Node number 108: 8 observations
## predicted class=N expected loss=0.375 P(node) =0.008196721
## class counts: 5 3
## probabilities: 0.625 0.375
##
## Node number 109: 36 observations
## predicted class=Y expected loss=0.3888889 P(node) =0.03688525
## class counts: 14 22
## probabilities: 0.389 0.611
##
## Node number 138: 209 observations, complexity param=0.0002217295
## predicted class=N expected loss=0.138756 P(node) =0.2141393
## class counts: 180 29
## probabilities: 0.861 0.139
## left son=276 (28 obs) right son=277 (181 obs)
## Primary splits:
## startprice.log < 6.223528 to the right, improve=0.6865651, (0 missing)
##
## Node number 139: 7 observations
## predicted class=N expected loss=0.4285714 P(node) =0.007172131
## class counts: 4 3
## probabilities: 0.571 0.429
##
## Node number 214: 42 observations, complexity param=0.002217295
## predicted class=Y expected loss=0.4761905 P(node) =0.04303279
## class counts: 20 22
## probabilities: 0.476 0.524
## left son=428 (14 obs) right son=429 (28 obs)
## Primary splits:
## startprice.log < 5.702096 to the right, improve=0.3809524, (0 missing)
##
## Node number 215: 9 observations
## predicted class=Y expected loss=0.2222222 P(node) =0.009221311
## class counts: 2 7
## probabilities: 0.222 0.778
##
## Node number 276: 28 observations
## predicted class=N expected loss=0.03571429 P(node) =0.02868852
## class counts: 27 1
## probabilities: 0.964 0.036
##
## Node number 277: 181 observations, complexity param=0.0002217295
## predicted class=N expected loss=0.1546961 P(node) =0.1854508
## class counts: 153 28
## probabilities: 0.845 0.155
## left son=554 (169 obs) right son=555 (12 obs)
## Primary splits:
## startprice.log < 6.212486 to the left, improve=0.8202513, (0 missing)
##
## Node number 428: 14 observations
## predicted class=N expected loss=0.4285714 P(node) =0.01434426
## class counts: 8 6
## probabilities: 0.571 0.429
##
## Node number 429: 28 observations, complexity param=0.002217295
## predicted class=Y expected loss=0.4285714 P(node) =0.02868852
## class counts: 12 16
## probabilities: 0.429 0.571
## left son=858 (21 obs) right son=859 (7 obs)
## Primary splits:
## startprice.log < 5.607448 to the left, improve=1.52381, (0 missing)
##
## Node number 554: 169 observations, complexity param=0.0002217295
## predicted class=N expected loss=0.1420118 P(node) =0.1731557
## class counts: 145 24
## probabilities: 0.858 0.142
## left son=1108 (21 obs) right son=1109 (148 obs)
## Primary splits:
## startprice.log < 6.086763 to the right, improve=0.4273187, (0 missing)
##
## Node number 555: 12 observations
## predicted class=N expected loss=0.3333333 P(node) =0.01229508
## class counts: 8 4
## probabilities: 0.667 0.333
##
## Node number 858: 21 observations, complexity param=0.002217295
## predicted class=N expected loss=0.4761905 P(node) =0.02151639
## class counts: 11 10
## probabilities: 0.524 0.476
## left son=1716 (7 obs) right son=1717 (14 obs)
## Primary splits:
## startprice.log < 5.550876 to the right, improve=0.7619048, (0 missing)
##
## Node number 859: 7 observations
## predicted class=Y expected loss=0.1428571 P(node) =0.007172131
## class counts: 1 6
## probabilities: 0.143 0.857
##
## Node number 1108: 21 observations
## predicted class=N expected loss=0.04761905 P(node) =0.02151639
## class counts: 20 1
## probabilities: 0.952 0.048
##
## Node number 1109: 148 observations, complexity param=0.0002217295
## predicted class=N expected loss=0.1554054 P(node) =0.1516393
## class counts: 125 23
## probabilities: 0.845 0.155
## left son=2218 (128 obs) right son=2219 (20 obs)
## Primary splits:
## startprice.log < 6.022218 to the left, improve=0.9669764, (0 missing)
##
## Node number 1716: 7 observations
## predicted class=N expected loss=0.2857143 P(node) =0.007172131
## class counts: 5 2
## probabilities: 0.714 0.286
##
## Node number 1717: 14 observations
## predicted class=Y expected loss=0.4285714 P(node) =0.01434426
## class counts: 6 8
## probabilities: 0.429 0.571
##
## Node number 2218: 128 observations
## predicted class=N expected loss=0.1328125 P(node) =0.1311475
## class counts: 111 17
## probabilities: 0.867 0.133
##
## Node number 2219: 20 observations, complexity param=0.0002217295
## predicted class=N expected loss=0.3 P(node) =0.0204918
## class counts: 14 6
## probabilities: 0.700 0.300
## left son=4438 (13 obs) right son=4439 (7 obs)
## Primary splits:
## startprice.log < 6.053253 to the right, improve=1.586813, (0 missing)
##
## Node number 4438: 13 observations
## predicted class=N expected loss=0.1538462 P(node) =0.01331967
## class counts: 11 2
## probabilities: 0.846 0.154
##
## Node number 4439: 7 observations
## predicted class=Y expected loss=0.4285714 P(node) =0.007172131
## class counts: 3 4
## probabilities: 0.429 0.571
##
## n= 976
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 976 451 N (0.53790984 0.46209016)
## 2) biddable< 0.5 539 121 N (0.77551020 0.22448980)
## 4) startprice.log>=5.220194 378 61 N (0.83862434 0.16137566)
## 8) startprice.log>=5.527188 273 36 N (0.86813187 0.13186813)
## 16) startprice.log< 5.634772 24 0 N (1.00000000 0.00000000) *
## 17) startprice.log>=5.634772 249 36 N (0.85542169 0.14457831)
## 34) startprice.log>=5.664694 242 33 N (0.86363636 0.13636364)
## 68) startprice.log>=6.440933 26 1 N (0.96153846 0.03846154) *
## 69) startprice.log< 6.440933 216 32 N (0.85185185 0.14814815)
## 138) startprice.log< 6.376704 209 29 N (0.86124402 0.13875598)
## 276) startprice.log>=6.223528 28 1 N (0.96428571 0.03571429) *
## 277) startprice.log< 6.223528 181 28 N (0.84530387 0.15469613)
## 554) startprice.log< 6.212486 169 24 N (0.85798817 0.14201183)
## 1108) startprice.log>=6.086763 21 1 N (0.95238095 0.04761905) *
## 1109) startprice.log< 6.086763 148 23 N (0.84459459 0.15540541)
## 2218) startprice.log< 6.022218 128 17 N (0.86718750 0.13281250) *
## 2219) startprice.log>=6.022218 20 6 N (0.70000000 0.30000000)
## 4438) startprice.log>=6.053253 13 2 N (0.84615385 0.15384615) *
## 4439) startprice.log< 6.053253 7 3 Y (0.42857143 0.57142857) *
## 555) startprice.log>=6.212486 12 4 N (0.66666667 0.33333333) *
## 139) startprice.log>=6.376704 7 3 N (0.57142857 0.42857143) *
## 35) startprice.log< 5.664694 7 3 N (0.57142857 0.42857143) *
## 9) startprice.log< 5.527188 105 25 N (0.76190476 0.23809524) *
## 5) startprice.log< 5.220194 161 60 N (0.62732919 0.37267081)
## 10) startprice.log< 4.867496 90 29 N (0.67777778 0.32222222)
## 20) startprice.log>=4.574498 35 6 N (0.82857143 0.17142857) *
## 21) startprice.log< 4.574498 55 23 N (0.58181818 0.41818182)
## 42) startprice.log< 3.365867 11 2 N (0.81818182 0.18181818) *
## 43) startprice.log>=3.365867 44 21 N (0.52272727 0.47727273)
## 86) startprice.log>=4.094261 33 13 N (0.60606061 0.39393939) *
## 87) startprice.log< 4.094261 11 3 Y (0.27272727 0.72727273) *
## 11) startprice.log>=4.867496 71 31 N (0.56338028 0.43661972)
## 22) startprice.log>=5.026653 40 12 N (0.70000000 0.30000000)
## 44) startprice.log< 5.176086 31 7 N (0.77419355 0.22580645) *
## 45) startprice.log>=5.176086 9 4 Y (0.44444444 0.55555556) *
## 23) startprice.log< 5.026653 31 12 Y (0.38709677 0.61290323)
## 46) startprice.log< 4.966276 9 4 N (0.55555556 0.44444444) *
## 47) startprice.log>=4.966276 22 7 Y (0.31818182 0.68181818) *
## 3) biddable>=0.5 437 107 Y (0.24485126 0.75514874)
## 6) startprice.log>=5.010402 159 79 N (0.50314465 0.49685535)
## 12) startprice.log>=6.024113 20 3 N (0.85000000 0.15000000) *
## 13) startprice.log< 6.024113 139 63 Y (0.45323741 0.54676259)
## 26) startprice.log>=5.327663 72 33 N (0.54166667 0.45833333)
## 52) startprice.log< 5.482701 13 1 N (0.92307692 0.07692308) *
## 53) startprice.log>=5.482701 59 27 Y (0.45762712 0.54237288)
## 106) startprice.log>=5.959529 8 3 N (0.62500000 0.37500000) *
## 107) startprice.log< 5.959529 51 22 Y (0.43137255 0.56862745)
## 214) startprice.log< 5.836219 42 20 Y (0.47619048 0.52380952)
## 428) startprice.log>=5.702096 14 6 N (0.57142857 0.42857143) *
## 429) startprice.log< 5.702096 28 12 Y (0.42857143 0.57142857)
## 858) startprice.log< 5.607448 21 10 N (0.52380952 0.47619048)
## 1716) startprice.log>=5.550876 7 2 N (0.71428571 0.28571429) *
## 1717) startprice.log< 5.550876 14 6 Y (0.42857143 0.57142857) *
## 859) startprice.log>=5.607448 7 1 Y (0.14285714 0.85714286) *
## 215) startprice.log>=5.836219 9 2 Y (0.22222222 0.77777778) *
## 27) startprice.log< 5.327663 67 24 Y (0.35820896 0.64179104)
## 54) startprice.log< 5.206629 44 19 Y (0.43181818 0.56818182)
## 108) startprice.log>=5.164643 8 3 N (0.62500000 0.37500000) *
## 109) startprice.log< 5.164643 36 14 Y (0.38888889 0.61111111) *
## 55) startprice.log>=5.206629 23 5 Y (0.21739130 0.78260870) *
## 7) startprice.log< 5.010402 278 27 Y (0.09712230 0.90287770) *
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320953
## 2 0.1 0.6798479
## 3 0.2 0.7527076
## 4 0.3 0.7937824
## 5 0.4 0.7964602
## 6 0.5 0.7935409
## 7 0.6 0.7813620
## 8 0.7 0.7445443
## 9 0.8 0.6983696
## 10 0.9 0.6886145
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 432
## 2 Y 91
## sold.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 93
## 2 360
## Prediction
## Reference N Y
## N 432 93
## Y 91 360
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.114754e-01 6.208893e-01 7.854988e-01 8.355569e-01 5.379098e-01
## AccuracyPValue McnemarPValue
## 7.376156e-72 9.412324e-01
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6321484
## 2 0.1 0.6611296
## 3 0.2 0.7033399
## 4 0.3 0.7460317
## 5 0.4 0.7490909
## 6 0.5 0.7515763
## 7 0.6 0.7385020
## 8 0.7 0.7358230
## 9 0.8 0.7246377
## 10 0.9 0.7203514
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 390
## 2 Y 111
## sold.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 86
## 2 298
## Prediction
## Reference N Y
## N 390 86
## Y 111 298
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.774011e-01 5.503014e-01 7.485294e-01 8.044124e-01 5.378531e-01
## AccuracyPValue McnemarPValue
## 1.304860e-49 8.727897e-02
## model_id model_method feats
## 1 Max.cor.Y.cv.0.cp.0.rpart rpart biddable, startprice.log
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 0.461 0.01
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8769591 0.4 0.7964602 0.8114754
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7854988 0.8355569 0.6208893 0.8257253
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.7515763 0.7774011
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7485294 0.8044124 0.5503014if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(model_id="Max.cor.Y",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Max.cor.Y.rpart"
## [1] " indep_vars: biddable, startprice.log"
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0155 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 976
##
## CP nsplit rel error
## 1 0.49445676 0 1.0000000
## 2 0.01552106 1 0.5055432
##
## Variable importance
## biddable startprice.log
## 70 30
##
## Node number 1: 976 observations, complexity param=0.4944568
## predicted class=N expected loss=0.4620902 P(node) =1
## class counts: 525 451
## probabilities: 0.538 0.462
## left son=2 (539 obs) right son=3 (437 obs)
## Primary splits:
## biddable < 0.5 to the left, improve=135.9194, (0 missing)
## startprice.log < 5.02015 to the right, improve=117.2326, (0 missing)
## Surrogate splits:
## startprice.log < 4.628135 to the right, agree=0.747, adj=0.435, (0 split)
##
## Node number 2: 539 observations
## predicted class=N expected loss=0.2244898 P(node) =0.5522541
## class counts: 418 121
## probabilities: 0.776 0.224
##
## Node number 3: 437 observations
## predicted class=Y expected loss=0.2448513 P(node) =0.4477459
## class counts: 107 330
## probabilities: 0.245 0.755
##
## n= 976
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 976 451 N (0.5379098 0.4620902)
## 2) biddable< 0.5 539 121 N (0.7755102 0.2244898) *
## 3) biddable>=0.5 437 107 Y (0.2448513 0.7551487) *
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320953
## 2 0.1 0.6320953
## 3 0.2 0.6320953
## 4 0.3 0.7432432
## 5 0.4 0.7432432
## 6 0.5 0.7432432
## 7 0.6 0.7432432
## 8 0.7 0.7432432
## 9 0.8 0.0000000
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.7000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Max.cor.Y.rpart.N
## 1 N 418
## 2 Y 121
## sold.fctr.predict.Max.cor.Y.rpart.Y
## 1 107
## 2 330
## Prediction
## Reference N Y
## N 418 107
## Y 121 330
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.663934e-01 5.290552e-01 7.385542e-01 7.926151e-01 5.379098e-01
## AccuracyPValue McnemarPValue
## 1.631860e-49 3.892678e-01
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6321484
## 2 0.1 0.6321484
## 3 0.2 0.6321484
## 4 0.3 0.7663782
## 5 0.4 0.7663782
## 6 0.5 0.7663782
## 7 0.6 0.7663782
## 8 0.7 0.7663782
## 9 0.8 0.0000000
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.7000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Max.cor.Y.rpart.N
## 1 N 386
## 2 Y 99
## sold.fctr.predict.Max.cor.Y.rpart.Y
## 1 90
## 2 310
## Prediction
## Reference N Y
## N 386 90
## Y 99 310
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.864407e-01 5.697529e-01 7.579436e-01 8.130160e-01 5.378531e-01
## AccuracyPValue McnemarPValue
## 1.590529e-53 5.606244e-01
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.rpart rpart biddable, startprice.log 3
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.993 0.011 0.7639489
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.7 0.7432432 0.7786629
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7385542 0.7926151 0.5507393 0.7844353
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.7 0.7663782 0.7864407
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7579436 0.813016 0.5697529
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.04549097 0.09113973# Used to compare vs. Interactions.High.cor.Y and/or Max.cor.Y.TmSrs
ret_lst <- myfit_mdl(model_id="Max.cor.Y",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Max.cor.Y.glm"
## [1] " indep_vars: biddable, startprice.log"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.6590 -0.7080 -0.4894 0.7798 2.1470
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.19127 0.52629 6.064 1.33e-09 ***
## biddable 1.64607 0.16602 9.915 < 2e-16 ***
## startprice.log -0.82751 0.09739 -8.497 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1347.41 on 975 degrees of freedom
## Residual deviance: 936.04 on 973 degrees of freedom
## AIC: 942.04
##
## Number of Fisher Scoring iterations: 6
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320953
## 2 0.1 0.6373938
## 3 0.2 0.7192374
## 4 0.3 0.7372709
## 5 0.4 0.7513691
## 6 0.5 0.7448276
## 7 0.6 0.7317676
## 8 0.7 0.6675862
## 9 0.8 0.5365079
## 10 0.9 0.3985891
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Max.cor.Y.glm.N
## 1 N 406
## 2 Y 108
## sold.fctr.predict.Max.cor.Y.glm.Y
## 1 119
## 2 343
## Prediction
## Reference N Y
## N 406 119
## Y 108 343
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.674180e-01 5.329494e-01 7.396162e-01 7.935960e-01 5.379098e-01
## AccuracyPValue McnemarPValue
## 5.764607e-50 5.068674e-01
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6321484
## 2 0.1 0.6365759
## 3 0.2 0.6885553
## 4 0.3 0.7319244
## 5 0.4 0.7668269
## 6 0.5 0.7674714
## 7 0.6 0.7388363
## 8 0.7 0.7025411
## 9 0.8 0.5630397
## 10 0.9 0.4137931
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Max.cor.Y.glm.N
## 1 N 400
## 2 Y 107
## sold.fctr.predict.Max.cor.Y.glm.Y
## 1 76
## 2 302
## Prediction
## Reference N Y
## N 400 76
## Y 107 302
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.932203e-01 5.818261e-01 7.650169e-01 8.194558e-01 5.378531e-01
## AccuracyPValue McnemarPValue
## 1.393813e-56 2.657776e-02
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.glm glm biddable, startprice.log 1
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.976 0.013 0.8417506
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.7513691 0.7725185
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7396162 0.793596 0.5401437 0.8357056
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.7674714 0.7932203
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7650169 0.8194558 0.5818261 942.0369
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.03487292 0.07025074if (!is.null(glb_date_vars) &&
(sum(grepl(paste(glb_date_vars, "\\.day\\.minutes\\.poly\\.", sep=""),
names(glb_allobs_df))) > 0)) {
# ret_lst <- myfit_mdl(model_id="Max.cor.Y.TmSrs.poly1",
# model_method=ifelse(glb_is_regression, "lm",
# ifelse(glb_is_binomial, "glm", "rpart")),
# model_type=glb_model_type,
# indep_vars_vctr=c(max_cor_y_x_vars, paste0(glb_date_vars, ".day.minutes")),
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
#
ret_lst <- myfit_mdl(model_id="Max.cor.Y.TmSrs.poly",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr=c(max_cor_y_x_vars,
grep(paste(glb_date_vars, "\\.day\\.minutes\\.poly\\.", sep=""),
names(glb_allobs_df), value=TRUE)),
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
}
# Interactions.High.cor.Y
if (length(int_feats <- setdiff(unique(glb_feats_df$cor.high.X), NA)) > 0) {
# lm & glm handle interaction terms; rpart & rf do not
if (glb_is_regression || glb_is_binomial) {
indep_vars_vctr <-
c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":"))
} else { indep_vars_vctr <- union(max_cor_y_x_vars, int_feats) }
ret_lst <- myfit_mdl(model_id="Interact.High.cor.Y",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr,
glb_rsp_var, glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
} ## [1] "fitting model: Interact.High.cor.Y.glm"
## [1] " indep_vars: biddable, startprice.log, biddable:D.nwrds.unq.log, biddable:D.T.like, biddable:D.npnct06.log, biddable:D.ratio.nstopwrds.nwrds, biddable:D.nchrs.log, biddable:D.nwrds.log, biddable:cellular.fctr"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.6677 -0.7023 -0.4843 0.7293 2.1577
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.30147 0.53553 6.165 7.05e-10
## biddable 3.67464 2.71615 1.353 0.1761
## startprice.log -0.84835 0.09920 -8.552 < 2e-16
## `biddable:D.nwrds.unq.log` -0.75281 1.72201 -0.437 0.6620
## `biddable:D.T.like` -0.74823 0.58216 -1.285 0.1987
## `biddable:D.npnct06.log` -0.07172 1.05018 -0.068 0.9456
## `biddable:D.ratio.nstopwrds.nwrds` -1.88078 2.70821 -0.694 0.4874
## `biddable:D.nchrs.log` -0.36019 1.13292 -0.318 0.7505
## `biddable:D.nwrds.log` 0.72729 2.19593 0.331 0.7405
## `biddable:cellular.fctr1` 0.02004 0.29234 0.069 0.9454
## `biddable:cellular.fctrUnknown` -0.93306 0.43475 -2.146 0.0319
##
## (Intercept) ***
## biddable
## startprice.log ***
## `biddable:D.nwrds.unq.log`
## `biddable:D.T.like`
## `biddable:D.npnct06.log`
## `biddable:D.ratio.nstopwrds.nwrds`
## `biddable:D.nchrs.log`
## `biddable:D.nwrds.log`
## `biddable:cellular.fctr1`
## `biddable:cellular.fctrUnknown` *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1347.4 on 975 degrees of freedom
## Residual deviance: 928.6 on 965 degrees of freedom
## AIC: 950.6
##
## Number of Fisher Scoring iterations: 6
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320953
## 2 0.1 0.6382979
## 3 0.2 0.7198612
## 4 0.3 0.7387755
## 5 0.4 0.7546755
## 6 0.5 0.7505774
## 7 0.6 0.7324291
## 8 0.7 0.6712707
## 9 0.8 0.5571205
## 10 0.9 0.4035088
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Interact.High.cor.Y.glm.N
## 1 N 410
## 2 Y 108
## sold.fctr.predict.Interact.High.cor.Y.glm.Y
## 1 115
## 2 343
## Prediction
## Reference N Y
## N 410 115
## Y 108 343
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.715164e-01 5.408929e-01 7.438660e-01 7.975178e-01 5.379098e-01
## AccuracyPValue McnemarPValue
## 8.476792e-52 6.878388e-01
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6321484
## 2 0.1 0.6365759
## 3 0.2 0.6885553
## 4 0.3 0.7315436
## 5 0.4 0.7651332
## 6 0.5 0.7634961
## 7 0.6 0.7486486
## 8 0.7 0.7076462
## 9 0.8 0.5854484
## 10 0.9 0.4473684
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Interact.High.cor.Y.glm.N
## 1 N 375
## 2 Y 93
## sold.fctr.predict.Interact.High.cor.Y.glm.Y
## 1 101
## 2 316
## Prediction
## Reference N Y
## N 375 101
## Y 93 316
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.807910e-01 5.596608e-01 7.520575e-01 8.076410e-01 5.378531e-01
## AccuracyPValue McnemarPValue
## 4.669336e-51 6.152661e-01
## model_id model_method
## 1 Interact.High.cor.Y.glm glm
## feats
## 1 biddable, startprice.log, biddable:D.nwrds.unq.log, biddable:D.T.like, biddable:D.npnct06.log, biddable:D.ratio.nstopwrds.nwrds, biddable:D.nchrs.log, biddable:D.nwrds.log, biddable:cellular.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 0.988 0.016
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8437145 0.4 0.7546755 0.7766305
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.743866 0.7975178 0.5477252 0.837
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.7651332 0.780791
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7520575 0.807641 0.5596608 950.5986
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.02572891 0.05220556# Low.cor.X
# if (glb_is_classification && glb_is_binomial)
# indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) &
# is.ConditionalX.y &
# (exclude.as.feat != 1))[, "id"] else
indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) & !myNearZV &
(exclude.as.feat != 1))[, "id"]
myadjust_interaction_feats <- function(vars_vctr) {
for (feat in subset(glb_feats_df, !is.na(interaction.feat))$id)
if (feat %in% vars_vctr)
vars_vctr <- union(setdiff(vars_vctr, feat),
paste0(glb_feats_df[glb_feats_df$id == feat, "interaction.feat"], ":", feat))
return(vars_vctr)
}
indep_vars_vctr <- myadjust_interaction_feats(indep_vars_vctr)
ret_lst <- myfit_mdl(model_id="Low.cor.X",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
glb_rsp_var, glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Low.cor.X.glm"
## [1] " indep_vars: biddable, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.P.air, D.T.use, storage.fctr, D.P.mini, D.T.condition, D.npnct11.log, .rnorm, D.T.ipad, D.npnct10.log, D.npnct08.log, D.T.condit, color.fctr, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.npnct09.log, D.nwrds.unq.log, D.ndgts.log, cellular.fctr, D.npnct14.log, prdline.my.fctr, D.npnct05.log, condition.fctr, startprice.log"
## Aggregating results
## Fitting final model on full training set
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.1319 -0.7289 -0.2184 0.6739 2.7148
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) 4.298e+00 1.007e+00 4.270
## biddable 1.539e+00 1.932e-01 7.965
## D.npnct15.log 6.480e-01 8.300e-01 0.781
## D.T.screen 1.687e-01 8.683e-01 0.194
## D.npnct03.log -1.392e-01 1.545e+00 -0.090
## D.T.used 1.451e-01 4.989e-01 0.291
## D.T.excellent 3.330e-01 3.750e-01 0.888
## D.T.great -1.369e+00 7.213e-01 -1.898
## D.ratio.sum.TfIdf.nwrds -1.815e-01 2.653e-01 -0.684
## D.T.good 7.490e-02 7.321e-01 0.102
## D.npnct01.log 1.059e+00 6.140e-01 1.725
## D.T.veri 2.225e-01 6.629e-01 0.336
## D.T.work 1.386e+00 6.695e-01 2.070
## D.T.scratch 7.905e-01 7.305e-01 1.082
## D.P.air 2.900e-02 1.013e+00 0.029
## D.T.use 7.940e-01 6.610e-01 1.201
## storage.fctr16 8.684e-01 5.162e-01 1.682
## storage.fctr32 6.709e-01 5.432e-01 1.235
## storage.fctr64 8.502e-01 5.297e-01 1.605
## storage.fctrUnknown 1.011e+00 7.164e-01 1.411
## D.P.mini -1.554e-01 1.066e+00 -0.146
## D.T.condition -1.043e+00 6.929e-01 -1.506
## D.npnct11.log 5.412e-02 3.630e-01 0.149
## .rnorm -5.131e-02 9.062e-02 -0.566
## D.T.ipad -2.426e+00 1.022e+00 -2.375
## D.npnct10.log 1.665e+00 1.491e+00 1.116
## D.npnct08.log -2.997e-01 7.493e-01 -0.400
## D.T.condit -3.416e-01 5.450e-01 -0.627
## color.fctrBlack -2.209e-01 2.455e-01 -0.900
## color.fctrGold -2.087e-01 5.265e-01 -0.396
## `color.fctrSpace Gray` 1.008e-01 3.213e-01 0.314
## color.fctrWhite -2.153e-01 2.393e-01 -0.900
## D.T.this 2.927e-01 9.942e-01 0.294
## D.npnct06.log -9.175e-01 9.573e-01 -0.958
## D.T.box -2.436e+00 1.095e+00 -2.224
## D.npnct28.log -3.264e-03 1.077e+03 0.000
## D.T.like -1.078e+00 5.684e-01 -1.896
## D.npnct12.log -1.689e-01 8.288e-01 -0.204
## D.npnct09.log -8.933e+00 1.003e+03 -0.009
## D.nwrds.unq.log 1.311e-02 1.520e-01 0.086
## D.ndgts.log 2.697e-01 4.382e-01 0.615
## cellular.fctr1 1.252e-02 2.107e-01 0.059
## cellular.fctrUnknown -3.914e-02 4.614e-01 -0.085
## D.npnct14.log -4.609e-02 7.713e-01 -0.060
## `prdline.my.fctriPad 2` 7.069e-01 3.641e-01 1.942
## `prdline.my.fctriPad 3+` 7.490e-01 3.564e-01 2.101
## prdline.my.fctriPadAir 1.244e+00 4.075e-01 3.052
## prdline.my.fctriPadmini 2.858e-02 3.615e-01 0.079
## `prdline.my.fctriPadmini 2+` 5.283e-01 4.344e-01 1.216
## prdline.my.fctrUnknown -7.255e-01 4.723e-01 -1.536
## D.npnct05.log -5.739e+00 3.441e+00 -1.668
## `condition.fctrFor parts or not working` -7.490e-01 3.600e-01 -2.080
## `condition.fctrManufacturer refurbished` -6.695e-01 7.623e-01 -0.878
## condition.fctrNew -9.782e-02 3.027e-01 -0.323
## `condition.fctrNew other (see details)` 6.794e-01 4.048e-01 1.678
## `condition.fctrSeller refurbished` -1.057e+00 4.387e-01 -2.409
## startprice.log -1.214e+00 1.668e-01 -7.276
## Pr(>|z|)
## (Intercept) 1.96e-05 ***
## biddable 1.66e-15 ***
## D.npnct15.log 0.43492
## D.T.screen 0.84597
## D.npnct03.log 0.92821
## D.T.used 0.77119
## D.T.excellent 0.37459
## D.T.great 0.05766 .
## D.ratio.sum.TfIdf.nwrds 0.49397
## D.T.good 0.91850
## D.npnct01.log 0.08445 .
## D.T.veri 0.73708
## D.T.work 0.03842 *
## D.T.scratch 0.27922
## D.P.air 0.97717
## D.T.use 0.22968
## storage.fctr16 0.09249 .
## storage.fctr32 0.21679
## storage.fctr64 0.10847
## storage.fctrUnknown 0.15814
## D.P.mini 0.88405
## D.T.condition 0.13210
## D.npnct11.log 0.88150
## .rnorm 0.57121
## D.T.ipad 0.01757 *
## D.npnct10.log 0.26432
## D.npnct08.log 0.68922
## D.T.condit 0.53082
## color.fctrBlack 0.36814
## color.fctrGold 0.69180
## `color.fctrSpace Gray` 0.75376
## color.fctrWhite 0.36824
## D.T.this 0.76845
## D.npnct06.log 0.33785
## D.T.box 0.02616 *
## D.npnct28.log 1.00000
## D.T.like 0.05795 .
## D.npnct12.log 0.83855
## D.npnct09.log 0.99289
## D.nwrds.unq.log 0.93124
## D.ndgts.log 0.53825
## cellular.fctr1 0.95259
## cellular.fctrUnknown 0.93241
## D.npnct14.log 0.95235
## `prdline.my.fctriPad 2` 0.05219 .
## `prdline.my.fctriPad 3+` 0.03560 *
## prdline.my.fctriPadAir 0.00227 **
## prdline.my.fctriPadmini 0.93697
## `prdline.my.fctriPadmini 2+` 0.22401
## prdline.my.fctrUnknown 0.12448
## D.npnct05.log 0.09529 .
## `condition.fctrFor parts or not working` 0.03749 *
## `condition.fctrManufacturer refurbished` 0.37985
## condition.fctrNew 0.74656
## `condition.fctrNew other (see details)` 0.09325 .
## `condition.fctrSeller refurbished` 0.01600 *
## startprice.log 3.43e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1347.41 on 975 degrees of freedom
## Residual deviance: 849.51 on 919 degrees of freedom
## AIC: 963.51
##
## Number of Fisher Scoring iterations: 14
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320953
## 2 0.1 0.6837209
## 3 0.2 0.7256792
## 4 0.3 0.7477920
## 5 0.4 0.7782609
## 6 0.5 0.7846333
## 7 0.6 0.7921760
## 8 0.7 0.7335092
## 9 0.8 0.6204819
## 10 0.9 0.4417808
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Low.cor.X.glm.N
## 1 N 482
## 2 Y 127
## sold.fctr.predict.Low.cor.X.glm.Y
## 1 43
## 2 324
## Prediction
## Reference N Y
## N 482 43
## Y 127 324
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.258197e-01 6.449648e-01 8.005408e-01 8.491145e-01 5.379098e-01
## AccuracyPValue McnemarPValue
## 4.216278e-80 1.942625e-10
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6321484
## 2 0.1 0.6537162
## 3 0.2 0.6959847
## 4 0.3 0.7176724
## 5 0.4 0.7434053
## 6 0.5 0.7650834
## 7 0.6 0.7452575
## 8 0.7 0.7188406
## 9 0.8 0.6550633
## 10 0.9 0.4881603
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Low.cor.X.glm.N
## 1 N 404
## 2 Y 111
## sold.fctr.predict.Low.cor.X.glm.Y
## 1 72
## 2 298
## Prediction
## Reference N Y
## N 404 72
## Y 111 298
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.932203e-01 5.812465e-01 7.650169e-01 8.194558e-01 5.378531e-01
## AccuracyPValue McnemarPValue
## 1.393813e-56 4.968934e-03
## model_id model_method
## 1 Low.cor.X.glm glm
## feats
## 1 biddable, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.P.air, D.T.use, storage.fctr, D.P.mini, D.T.condition, D.npnct11.log, .rnorm, D.T.ipad, D.npnct10.log, D.npnct08.log, D.T.condit, color.fctr, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.npnct09.log, D.nwrds.unq.log, D.ndgts.log, cellular.fctr, D.npnct14.log, prdline.my.fctr, D.npnct05.log, condition.fctr, startprice.log
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.247 0.128
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8721233 0.6 0.792176 0.7551046
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8005408 0.8491145 0.505872 0.8289074
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.7650834 0.7932203
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7650169 0.8194558 0.5812465 963.5142
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01887657 0.03908066rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 10 fit.models 7 0 42.675 68.915 26.24
## 11 fit.models 7 1 68.916 NA NAfit.models_1_chunk_df <- myadd_chunk(NULL, "fit.models_1_bgn")## label step_major step_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 73.246 NA NA# Options:
# 1. rpart & rf manual tuning
# 2. rf without pca (default: with pca)
#stop(here"); sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df
#glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df
# All X that is not user excluded
# if (glb_is_classification && glb_is_binomial) {
# model_id_pfx <- "Conditional.X"
# # indep_vars_vctr <- setdiff(names(glb_fitobs_df), union(glb_rsp_var, glb_exclude_vars_as_features))
# indep_vars_vctr <- subset(glb_feats_df, is.ConditionalX.y &
# (exclude.as.feat != 1))[, "id"]
# } else {
model_id_pfx <- "All.X"
indep_vars_vctr <- subset(glb_feats_df, !myNearZV &
(exclude.as.feat != 1))[, "id"]
# }
indep_vars_vctr <- myadjust_interaction_feats(indep_vars_vctr)
for (method in glb_models_method_vctr) {
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df,
paste0("fit.models_1_", method), major.inc=TRUE)
if (method %in% c("rpart", "rf")) {
# rpart: fubar's the tree
# rf: skip the scenario w/ .rnorm for speed
indep_vars_vctr <- setdiff(indep_vars_vctr, c(".rnorm"))
model_id <- paste0(model_id_pfx, ".no.rnorm")
} else model_id <- model_id_pfx
ret_lst <- myfit_mdl(model_id=model_id, model_method=method,
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
# If All.X.glm is less accurate than Low.Cor.X.glm
# check NA coefficients & filter appropriate terms in indep_vars_vctr
# if (method == "glm") {
# orig_glm <- glb_models_lst[[paste0(model_id, ".", model_method)]]$finalModel
# orig_glm <- glb_models_lst[["All.X.glm"]]$finalModel; print(summary(orig_glm))
# vif_orig_glm <- vif(orig_glm); print(vif_orig_glm)
# print(vif_orig_glm[!is.na(vif_orig_glm) & (vif_orig_glm == Inf)])
# print(which.max(vif_orig_glm))
# print(sort(vif_orig_glm[vif_orig_glm >= 1.0e+03], decreasing=TRUE))
# glb_fitobs_df[c(1143, 3637, 3953, 4105), c("UniqueID", "Popular", "H.P.quandary", "Headline")]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.nchrs.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.nchrs.log", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.npnct14.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.npnct14.log", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.T.scen", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.T.scen", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.P.first", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.P.first", glb_feats_df$id, value=TRUE), ]
# all.equal(glb_allobs_df$S.nuppr.log, glb_allobs_df$A.nuppr.log)
# all.equal(glb_allobs_df$S.npnct19.log, glb_allobs_df$A.npnct19.log)
# all.equal(glb_allobs_df$S.P.year.colon, glb_allobs_df$A.P.year.colon)
# all.equal(glb_allobs_df$S.T.share, glb_allobs_df$A.T.share)
# all.equal(glb_allobs_df$H.T.clip, glb_allobs_df$H.P.daily.clip.report)
# cor(glb_allobs_df$S.T.herald, glb_allobs_df$S.T.tribun)
# dsp_obs(Abstract.contains="[Dd]iar", cols=("Abstract"), all=TRUE)
# dsp_obs(Abstract.contains="[Ss]hare", cols=("Abstract"), all=TRUE)
# subset(glb_feats_df, cor.y.abs <= glb_feats_df[glb_feats_df$id == ".rnorm", "cor.y.abs"])
# corxx_mtrx <- cor(data.matrix(glb_allobs_df[, setdiff(names(glb_allobs_df), myfind_chr_cols_df(glb_allobs_df))]), use="pairwise.complete.obs"); abs_corxx_mtrx <- abs(corxx_mtrx); diag(abs_corxx_mtrx) <- 0
# which.max(abs_corxx_mtrx["S.T.tribun", ])
# abs_corxx_mtrx["A.npnct08.log", "S.npnct08.log"]
# step_glm <- step(orig_glm)
# }
# Since caret does not optimize rpart well
# if (method == "rpart")
# ret_lst <- myfit_mdl(model_id=paste0(model_id_pfx, ".cp.0"), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))
}## label step_major step_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 73.246 73.261 0.016
## 2 fit.models_1_glm 2 0 73.262 NA NA
## [1] "fitting model: All.X.glm"
## [1] " indep_vars: biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.P.air, D.T.use, storage.fctr, D.P.mini, D.T.condition, D.npnct11.log, .rnorm, D.T.ipad, D.npnct10.log, D.sum.TfIdf, D.T.new, D.npnct13.log, D.npnct08.log, D.T.condit, color.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.nwrds.unq.log, D.ndgts.log, carrier.fctr, cellular.fctr, D.npnct14.log, prdline.my.fctr, D.npnct05.log, condition.fctr, startprice.log"
## Aggregating results
## Fitting final model on full training set
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.3120 -0.6911 -0.2036 0.6496 2.7453
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error z value
## (Intercept) 9.18049 6.82415 1.345
## biddable 1.51023 0.19778 7.636
## D.ratio.nstopwrds.nwrds -4.63896 6.75058 -0.687
## D.npnct15.log -0.20585 0.98068 -0.210
## D.T.screen 0.43536 0.89156 0.488
## D.npnct03.log -0.40337 1.78378 -0.226
## D.T.used -0.16793 0.80382 -0.209
## D.T.excellent 1.17058 0.57081 2.051
## D.T.great -1.28538 0.77995 -1.648
## D.ratio.sum.TfIdf.nwrds -1.67482 1.02047 -1.641
## D.T.good 0.27101 0.78594 0.345
## D.npnct01.log 0.45897 0.80664 0.569
## D.T.veri 0.49554 0.72996 0.679
## D.T.work 1.71006 0.69296 2.468
## D.T.scratch 1.40583 0.83326 1.687
## D.P.air -0.21563 1.06059 -0.203
## D.T.use 1.33399 0.81958 1.628
## storage.fctr16 0.67818 0.51670 1.313
## storage.fctr32 0.52438 0.54686 0.959
## storage.fctr64 0.70518 0.52886 1.333
## storage.fctrUnknown 0.96522 0.71918 1.342
## D.P.mini -0.03574 1.14488 -0.031
## D.T.condition -0.70970 0.91461 -0.776
## D.npnct11.log -0.01088 0.43233 -0.025
## .rnorm -0.03659 0.09188 -0.398
## D.T.ipad -2.29715 1.08462 -2.118
## D.npnct10.log 1.62977 1.51645 1.075
## D.sum.TfIdf 0.53674 0.20454 2.624
## D.T.new 0.02301 0.93711 0.025
## D.npnct13.log -0.62462 0.50955 -1.226
## D.npnct08.log -0.47685 0.83882 -0.568
## D.T.condit 0.23178 0.72623 0.319
## color.fctrBlack -0.21570 0.25178 -0.857
## color.fctrGold -0.27824 0.54546 -0.510
## `color.fctrSpace Gray` 0.07988 0.32785 0.244
## color.fctrWhite -0.19112 0.24840 -0.769
## D.nstopwrds.log 1.63792 1.99649 0.820
## D.npnct16.log 1.14605 1.78856 0.641
## D.npnct24.log 1.36912 9.23244 0.148
## D.T.this 0.35224 1.14582 0.307
## D.npnct06.log -2.39569 2.01929 -1.186
## D.T.box -2.41320 1.10098 -2.192
## D.npnct28.log 0.10572 1178.10730 0.000
## D.T.like -0.98631 0.85875 -1.149
## D.npnct12.log -0.23981 0.86795 -0.276
## D.nuppr.log -6.22448 7.05019 -0.883
## D.nchrs.log 5.17118 8.29155 0.624
## D.nwrds.log -1.45355 3.05696 -0.475
## D.npnct09.log -9.23954 1078.48382 -0.009
## D.nwrds.unq.log -0.75253 0.90543 -0.831
## D.ndgts.log 0.06750 0.60890 0.111
## `carrier.fctrAT&T` -0.05654 0.65683 -0.086
## carrier.fctrOther 18.56034 834.73038 0.022
## carrier.fctrSprint 0.58427 0.86062 0.679
## `carrier.fctrT-Mobile` -0.98009 1.07619 -0.911
## carrier.fctrUnknown -0.09227 0.47347 -0.195
## carrier.fctrVerizon 0.11003 0.68973 0.160
## cellular.fctr1 0.01521 0.59613 0.026
## cellular.fctrUnknown NA NA NA
## D.npnct14.log -0.55778 0.82336 -0.677
## `prdline.my.fctriPad 2` 0.74835 0.37497 1.996
## `prdline.my.fctriPad 3+` 0.74214 0.36423 2.038
## prdline.my.fctriPadAir 1.25248 0.41626 3.009
## prdline.my.fctriPadmini -0.01295 0.36950 -0.035
## `prdline.my.fctriPadmini 2+` 0.55549 0.44361 1.252
## prdline.my.fctrUnknown -0.74467 0.48197 -1.545
## D.npnct05.log -5.49462 3.87031 -1.420
## `condition.fctrFor parts or not working` -0.84728 0.37070 -2.286
## `condition.fctrManufacturer refurbished` -0.71843 0.77816 -0.923
## condition.fctrNew -0.10614 0.30454 -0.349
## `condition.fctrNew other (see details)` 0.55209 0.44500 1.241
## `condition.fctrSeller refurbished` -0.91484 0.44953 -2.035
## startprice.log -1.22661 0.16467 -7.449
## Pr(>|z|)
## (Intercept) 0.17853
## biddable 2.24e-14 ***
## D.ratio.nstopwrds.nwrds 0.49196
## D.npnct15.log 0.83374
## D.T.screen 0.62533
## D.npnct03.log 0.82110
## D.T.used 0.83451
## D.T.excellent 0.04029 *
## D.T.great 0.09935 .
## D.ratio.sum.TfIdf.nwrds 0.10075
## D.T.good 0.73022
## D.npnct01.log 0.56936
## D.T.veri 0.49723
## D.T.work 0.01360 *
## D.T.scratch 0.09157 .
## D.P.air 0.83889
## D.T.use 0.10360
## storage.fctr16 0.18934
## storage.fctr32 0.33761
## storage.fctr64 0.18240
## storage.fctrUnknown 0.17956
## D.P.mini 0.97510
## D.T.condition 0.43777
## D.npnct11.log 0.97992
## .rnorm 0.69043
## D.T.ipad 0.03418 *
## D.npnct10.log 0.28250
## D.sum.TfIdf 0.00869 **
## D.T.new 0.98041
## D.npnct13.log 0.22027
## D.npnct08.log 0.56971
## D.T.condit 0.74960
## color.fctrBlack 0.39161
## color.fctrGold 0.60997
## `color.fctrSpace Gray` 0.80751
## color.fctrWhite 0.44166
## D.nstopwrds.log 0.41199
## D.npnct16.log 0.52167
## D.npnct24.log 0.88211
## D.T.this 0.75853
## D.npnct06.log 0.23546
## D.T.box 0.02839 *
## D.npnct28.log 0.99993
## D.T.like 0.25074
## D.npnct12.log 0.78233
## D.nuppr.log 0.37730
## D.nchrs.log 0.53285
## D.nwrds.log 0.63444
## D.npnct09.log 0.99316
## D.nwrds.unq.log 0.40590
## D.ndgts.log 0.91173
## `carrier.fctrAT&T` 0.93141
## carrier.fctrOther 0.98226
## carrier.fctrSprint 0.49721
## `carrier.fctrT-Mobile` 0.36245
## carrier.fctrUnknown 0.84549
## carrier.fctrVerizon 0.87325
## cellular.fctr1 0.97965
## cellular.fctrUnknown NA
## D.npnct14.log 0.49813
## `prdline.my.fctriPad 2` 0.04596 *
## `prdline.my.fctriPad 3+` 0.04159 *
## prdline.my.fctriPadAir 0.00262 **
## prdline.my.fctriPadmini 0.97204
## `prdline.my.fctriPadmini 2+` 0.21050
## prdline.my.fctrUnknown 0.12233
## D.npnct05.log 0.15570
## `condition.fctrFor parts or not working` 0.02228 *
## `condition.fctrManufacturer refurbished` 0.35588
## condition.fctrNew 0.72744
## `condition.fctrNew other (see details)` 0.21473
## `condition.fctrSeller refurbished` 0.04184 *
## startprice.log 9.40e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1347.4 on 975 degrees of freedom
## Residual deviance: 832.7 on 904 degrees of freedom
## AIC: 976.7
##
## Number of Fisher Scoring iterations: 14
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.6320953
## 2 0.1 0.6869159
## 3 0.2 0.7361854
## 4 0.3 0.7623762
## 5 0.4 0.7887324
## 6 0.5 0.7851336
## 7 0.6 0.7882497
## 8 0.7 0.7339450
## 9 0.8 0.6360947
## 10 0.9 0.4576271
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.All.X.glm.N sold.fctr.predict.All.X.glm.Y
## 1 N 417 108
## 2 Y 87 364
## Prediction
## Reference N Y
## N 417 108
## Y 87 364
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.002049e-01 5.994140e-01 7.737180e-01 8.248664e-01 5.379098e-01
## AccuracyPValue McnemarPValue
## 8.793905e-66 1.520781e-01
## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.6321484
## 2 0.1 0.6531303
## 3 0.2 0.6902486
## 4 0.3 0.7196563
## 5 0.4 0.7380952
## 6 0.5 0.7569620
## 7 0.6 0.7453083
## 8 0.7 0.7167630
## 9 0.8 0.6432000
## 10 0.9 0.5169340
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.All.X.glm.N sold.fctr.predict.All.X.glm.Y
## 1 N 394 82
## 2 Y 110 299
## Prediction
## Reference N Y
## N 394 82
## Y 110 299
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.830508e-01 5.614877e-01 7.544110e-01 8.097919e-01 5.378531e-01
## AccuracyPValue McnemarPValue
## 4.906856e-52 5.134833e-02
## model_id model_method
## 1 All.X.glm glm
## feats
## 1 biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.P.air, D.T.use, storage.fctr, D.P.mini, D.T.condition, D.npnct11.log, .rnorm, D.T.ipad, D.npnct10.log, D.sum.TfIdf, D.T.new, D.npnct13.log, D.npnct08.log, D.T.condit, color.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.nwrds.unq.log, D.ndgts.log, carrier.fctr, cellular.fctr, D.npnct14.log, prdline.my.fctr, D.npnct05.log, condition.fctr, startprice.log
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.356 0.164
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8793749 0.4 0.7887324 0.7530628
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.773718 0.8248664 0.5017193 0.828178
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.756962 0.7830508
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.754411 0.8097919 0.5614877 976.6992
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01797809 0.03832757
## label step_major step_minor bgn end elapsed
## 2 fit.models_1_glm 2 0 73.262 78.427 5.165
## 3 fit.models_1_bayesglm 3 0 78.427 NA NA
## [1] "fitting model: All.X.bayesglm"
## [1] " indep_vars: biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.P.air, D.T.use, storage.fctr, D.P.mini, D.T.condition, D.npnct11.log, .rnorm, D.T.ipad, D.npnct10.log, D.sum.TfIdf, D.T.new, D.npnct13.log, D.npnct08.log, D.T.condit, color.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.nwrds.unq.log, D.ndgts.log, carrier.fctr, cellular.fctr, D.npnct14.log, prdline.my.fctr, D.npnct05.log, condition.fctr, startprice.log"## Loading required package: arm
## Loading required package: MASS
##
## Attaching package: 'MASS'
##
## The following object is masked from 'package:dplyr':
##
## select
##
## Loading required package: Matrix
## Loading required package: lme4
##
## arm (Version 1.8-6, built: 2015-7-7)
##
## Working directory is /Users/bbalaji-2012/Documents/Work/Courses/MIT/Analytics_Edge_15_071x/Assignments/Kaggle_eBay_iPads
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.2049 -0.7093 -0.2346 0.6715 2.7063
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) 4.29380 2.38323 1.802
## biddable 1.50367 0.19304 7.789
## D.ratio.nstopwrds.nwrds 0.26142 2.19234 0.119
## D.npnct15.log 0.16463 0.89443 0.184
## D.T.screen 0.37612 0.86439 0.435
## D.npnct03.log -0.62210 1.62857 -0.382
## D.T.used 0.30380 0.56669 0.536
## D.T.excellent 0.65163 0.43207 1.508
## D.T.great -1.20466 0.73450 -1.640
## D.ratio.sum.TfIdf.nwrds -0.79823 0.52303 -1.526
## D.T.good 0.19422 0.74839 0.260
## D.npnct01.log 0.72031 0.64090 1.124
## D.T.veri 0.38966 0.67975 0.573
## D.T.work 1.57551 0.67023 2.351
## D.T.scratch 1.21488 0.76856 1.581
## D.P.air 0.01590 1.01870 0.016
## D.T.use 1.15086 0.71851 1.602
## storage.fctr16 0.54653 0.44441 1.230
## storage.fctr32 0.37016 0.47544 0.779
## storage.fctr64 0.57453 0.45914 1.251
## storage.fctrUnknown 0.75212 0.63014 1.194
## D.P.mini 0.14354 0.93339 0.154
## D.T.condition -0.64352 0.74133 -0.868
## D.npnct11.log 0.04158 0.37681 0.110
## .rnorm -0.04120 0.08992 -0.458
## D.T.ipad -2.30990 1.02949 -2.244
## D.npnct10.log 1.74382 1.52734 1.142
## D.sum.TfIdf 0.38043 0.16175 2.352
## D.T.new 0.34789 0.87004 0.400
## D.npnct13.log -0.37975 0.38185 -0.994
## D.npnct08.log -0.35148 0.78554 -0.447
## D.T.condit 0.01775 0.60732 0.029
## color.fctrBlack -0.20486 0.24390 -0.840
## color.fctrGold -0.22522 0.50835 -0.443
## `color.fctrSpace Gray` 0.09709 0.31642 0.307
## color.fctrWhite -0.20181 0.23895 -0.845
## D.nstopwrds.log 0.26292 0.62035 0.424
## D.npnct16.log 0.99954 1.52229 0.657
## D.npnct24.log 0.45728 2.60865 0.175
## D.T.this 0.04240 1.07181 0.040
## D.npnct06.log -1.70251 1.74082 -0.978
## D.T.box -2.35839 1.09635 -2.151
## D.npnct28.log -0.69913 9.92183 -0.070
## D.T.like -0.97209 0.82551 -1.178
## D.npnct12.log -0.25430 0.80150 -0.317
## D.nuppr.log -0.18805 0.51457 -0.365
## D.nchrs.log -0.10293 0.48910 -0.210
## D.nwrds.log -0.02075 0.73774 -0.028
## D.npnct09.log -1.68700 6.34351 -0.266
## D.nwrds.unq.log -0.58925 0.59069 -0.998
## D.ndgts.log 0.30452 0.46737 0.652
## `carrier.fctrAT&T` -0.11900 0.75426 -0.158
## carrier.fctrOther 1.28407 1.84777 0.695
## carrier.fctrSprint 0.43044 0.87944 0.489
## `carrier.fctrT-Mobile` -0.86637 1.00109 -0.865
## carrier.fctrUnknown -0.16440 0.75713 -0.217
## carrier.fctrVerizon 0.02061 0.77431 0.027
## cellular.fctr1 0.07229 0.72688 0.099
## cellular.fctrUnknown 0.11071 0.83898 0.132
## D.npnct14.log -0.40218 0.77333 -0.520
## `prdline.my.fctriPad 2` 0.67499 0.35285 1.913
## `prdline.my.fctriPad 3+` 0.66905 0.34197 1.956
## prdline.my.fctriPadAir 1.16462 0.38869 2.996
## prdline.my.fctriPadmini -0.03891 0.34562 -0.113
## `prdline.my.fctriPadmini 2+` 0.47117 0.41379 1.139
## prdline.my.fctrUnknown -0.74187 0.45293 -1.638
## D.npnct05.log -3.45149 2.13292 -1.618
## `condition.fctrFor parts or not working` -0.76521 0.35629 -2.148
## `condition.fctrManufacturer refurbished` -0.61118 0.69934 -0.874
## condition.fctrNew -0.11799 0.29636 -0.398
## `condition.fctrNew other (see details)` 0.57500 0.41411 1.389
## `condition.fctrSeller refurbished` -0.92328 0.42989 -2.148
## startprice.log -1.19395 0.16209 -7.366
## Pr(>|z|)
## (Intercept) 0.07160 .
## biddable 6.73e-15 ***
## D.ratio.nstopwrds.nwrds 0.90508
## D.npnct15.log 0.85397
## D.T.screen 0.66347
## D.npnct03.log 0.70247
## D.T.used 0.59189
## D.T.excellent 0.13151
## D.T.great 0.10098
## D.ratio.sum.TfIdf.nwrds 0.12697
## D.T.good 0.79524
## D.npnct01.log 0.26105
## D.T.veri 0.56649
## D.T.work 0.01874 *
## D.T.scratch 0.11394
## D.P.air 0.98755
## D.T.use 0.10922
## storage.fctr16 0.21878
## storage.fctr32 0.43624
## storage.fctr64 0.21082
## storage.fctrUnknown 0.23265
## D.P.mini 0.87778
## D.T.condition 0.38536
## D.npnct11.log 0.91214
## .rnorm 0.64684
## D.T.ipad 0.02485 *
## D.npnct10.log 0.25356
## D.sum.TfIdf 0.01868 *
## D.T.new 0.68926
## D.npnct13.log 0.31998
## D.npnct08.log 0.65456
## D.T.condit 0.97668
## color.fctrBlack 0.40093
## color.fctrGold 0.65773
## `color.fctrSpace Gray` 0.75897
## color.fctrWhite 0.39836
## D.nstopwrds.log 0.67169
## D.npnct16.log 0.51144
## D.npnct24.log 0.86085
## D.T.this 0.96844
## D.npnct06.log 0.32808
## D.T.box 0.03147 *
## D.npnct28.log 0.94382
## D.T.like 0.23897
## D.npnct12.log 0.75103
## D.nuppr.log 0.71478
## D.nchrs.log 0.83331
## D.nwrds.log 0.97756
## D.npnct09.log 0.79028
## D.nwrds.unq.log 0.31849
## D.ndgts.log 0.51468
## `carrier.fctrAT&T` 0.87464
## carrier.fctrOther 0.48710
## carrier.fctrSprint 0.62453
## `carrier.fctrT-Mobile` 0.38680
## carrier.fctrUnknown 0.82810
## carrier.fctrVerizon 0.97877
## cellular.fctr1 0.92078
## cellular.fctrUnknown 0.89502
## D.npnct14.log 0.60302
## `prdline.my.fctriPad 2` 0.05575 .
## `prdline.my.fctriPad 3+` 0.05041 .
## prdline.my.fctriPadAir 0.00273 **
## prdline.my.fctriPadmini 0.91037
## `prdline.my.fctriPadmini 2+` 0.25484
## prdline.my.fctrUnknown 0.10144
## D.npnct05.log 0.10562
## `condition.fctrFor parts or not working` 0.03174 *
## `condition.fctrManufacturer refurbished` 0.38216
## condition.fctrNew 0.69052
## `condition.fctrNew other (see details)` 0.16497
## `condition.fctrSeller refurbished` 0.03173 *
## startprice.log 1.76e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1347.41 on 975 degrees of freedom
## Residual deviance: 836.96 on 903 degrees of freedom
## AIC: 982.96
##
## Number of Fisher Scoring iterations: 15
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320953
## 2 0.1 0.6831530
## 3 0.2 0.7301587
## 4 0.3 0.7605911
## 5 0.4 0.7762162
## 6 0.5 0.7865429
## 7 0.6 0.7897311
## 8 0.7 0.7332457
## 9 0.8 0.6184012
## 10 0.9 0.4444444
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.All.X.bayesglm.N
## 1 N 481
## 2 Y 128
## sold.fctr.predict.All.X.bayesglm.Y
## 1 44
## 2 323
## Prediction
## Reference N Y
## N 481 44
## Y 128 323
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.237705e-01 6.407880e-01 7.983885e-01 8.471812e-01 5.379098e-01
## AccuracyPValue McnemarPValue
## 6.895728e-79 2.472502e-10
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6321484
## 2 0.1 0.6532999
## 3 0.2 0.6947368
## 4 0.3 0.7221622
## 5 0.4 0.7368421
## 6 0.5 0.7649301
## 7 0.6 0.7496617
## 8 0.7 0.7206946
## 9 0.8 0.6278317
## 10 0.9 0.4909091
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.All.X.bayesglm.N
## 1 N 399
## 2 Y 108
## sold.fctr.predict.All.X.bayesglm.Y
## 1 77
## 2 301
## Prediction
## Reference N Y
## N 399 77
## Y 108 301
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.909605e-01 5.772559e-01 7.626579e-01 8.173104e-01 5.378531e-01
## AccuracyPValue McnemarPValue
## 1.496581e-55 2.740895e-02
## model_id model_method
## 1 All.X.bayesglm bayesglm
## feats
## 1 biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.P.air, D.T.use, storage.fctr, D.P.mini, D.T.condition, D.npnct11.log, .rnorm, D.T.ipad, D.npnct10.log, D.sum.TfIdf, D.T.new, D.npnct13.log, D.npnct08.log, D.T.condit, color.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.nwrds.unq.log, D.ndgts.log, carrier.fctr, cellular.fctr, D.npnct14.log, prdline.my.fctr, D.npnct05.log, condition.fctr, startprice.log
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 2.307 0.271
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8773815 0.6 0.7897311 0.7571433
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7983885 0.8471812 0.5093883 0.8305151
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.7649301 0.7909605
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7626579 0.8173104 0.5772559 982.9554
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.03574371 0.07291062
## label step_major step_minor bgn end elapsed
## 3 fit.models_1_bayesglm 3 0 78.427 83.892 5.465
## 4 fit.models_1_rpart 4 0 83.893 NA NA
## [1] "fitting model: All.X.no.rnorm.rpart"
## [1] " indep_vars: biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.P.air, D.T.use, storage.fctr, D.P.mini, D.T.condition, D.npnct11.log, D.T.ipad, D.npnct10.log, D.sum.TfIdf, D.T.new, D.npnct13.log, D.npnct08.log, D.T.condit, color.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.nwrds.unq.log, D.ndgts.log, carrier.fctr, cellular.fctr, D.npnct14.log, prdline.my.fctr, D.npnct05.log, condition.fctr, startprice.log"
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0266 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 976
##
## CP nsplit rel error
## 1 0.49445676 0 1.0000000
## 2 0.02660754 1 0.5055432
##
## Variable importance
## biddable startprice.log D.ratio.sum.TfIdf.nwrds
## 66 29 2
## prdline.my.fctriPadmini D.T.great D.T.screen
## 2 1 1
##
## Node number 1: 976 observations, complexity param=0.4944568
## predicted class=N expected loss=0.4620902 P(node) =1
## class counts: 525 451
## probabilities: 0.538 0.462
## left son=2 (539 obs) right son=3 (437 obs)
## Primary splits:
## biddable < 0.5 to the left, improve=135.919400, (0 missing)
## startprice.log < 5.02015 to the right, improve=117.232600, (0 missing)
## condition.fctrNew < 0.5 to the right, improve= 11.614270, (0 missing)
## D.npnct05.log < 0.3465736 to the right, improve= 6.629592, (0 missing)
## D.nwrds.unq.log < 2.249905 to the right, improve= 4.816042, (0 missing)
## Surrogate splits:
## startprice.log < 4.628135 to the right, agree=0.747, adj=0.435, (0 split)
## D.ratio.sum.TfIdf.nwrds < 0.8721891 to the left, agree=0.564, adj=0.025, (0 split)
## prdline.my.fctriPadmini < 0.5 to the left, agree=0.564, adj=0.025, (0 split)
## D.T.great < 0.4266606 to the left, agree=0.561, adj=0.021, (0 split)
## D.T.screen < 0.4356448 to the left, agree=0.560, adj=0.018, (0 split)
##
## Node number 2: 539 observations
## predicted class=N expected loss=0.2244898 P(node) =0.5522541
## class counts: 418 121
## probabilities: 0.776 0.224
##
## Node number 3: 437 observations
## predicted class=Y expected loss=0.2448513 P(node) =0.4477459
## class counts: 107 330
## probabilities: 0.245 0.755
##
## n= 976
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 976 451 N (0.5379098 0.4620902)
## 2) biddable< 0.5 539 121 N (0.7755102 0.2244898) *
## 3) biddable>=0.5 437 107 Y (0.2448513 0.7551487) *
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320953
## 2 0.1 0.6320953
## 3 0.2 0.6320953
## 4 0.3 0.7432432
## 5 0.4 0.7432432
## 6 0.5 0.7432432
## 7 0.6 0.7432432
## 8 0.7 0.7432432
## 9 0.8 0.0000000
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.7000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.All.X.no.rnorm.rpart.N
## 1 N 418
## 2 Y 121
## sold.fctr.predict.All.X.no.rnorm.rpart.Y
## 1 107
## 2 330
## Prediction
## Reference N Y
## N 418 107
## Y 121 330
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.663934e-01 5.290552e-01 7.385542e-01 7.926151e-01 5.379098e-01
## AccuracyPValue McnemarPValue
## 1.631860e-49 3.892678e-01
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6321484
## 2 0.1 0.6321484
## 3 0.2 0.6321484
## 4 0.3 0.7663782
## 5 0.4 0.7663782
## 6 0.5 0.7663782
## 7 0.6 0.7663782
## 8 0.7 0.7663782
## 9 0.8 0.0000000
## 10 0.9 0.0000000
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.7000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.All.X.no.rnorm.rpart.N
## 1 N 386
## 2 Y 99
## sold.fctr.predict.All.X.no.rnorm.rpart.Y
## 1 90
## 2 310
## Prediction
## Reference N Y
## N 386 90
## Y 99 310
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.864407e-01 5.697529e-01 7.579436e-01 8.130160e-01 5.378531e-01
## AccuracyPValue McnemarPValue
## 1.590529e-53 5.606244e-01
## model_id model_method
## 1 All.X.no.rnorm.rpart rpart
## feats
## 1 biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.P.air, D.T.use, storage.fctr, D.P.mini, D.T.condition, D.npnct11.log, D.T.ipad, D.npnct10.log, D.sum.TfIdf, D.T.new, D.npnct13.log, D.npnct08.log, D.T.condit, color.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.nwrds.unq.log, D.ndgts.log, carrier.fctr, cellular.fctr, D.npnct14.log, prdline.my.fctr, D.npnct05.log, condition.fctr, startprice.log
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 1.389 0.057
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.7639489 0.7 0.7432432 0.7786755
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7385542 0.7926151 0.5490774 0.7844353
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.7 0.7663782 0.7864407
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7579436 0.813016 0.5697529
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.03268626 0.0682405
## label step_major step_minor bgn end elapsed
## 4 fit.models_1_rpart 4 0 83.893 88.995 5.103
## 5 fit.models_1_rf 5 0 88.996 NA NA
## [1] "fitting model: All.X.no.rnorm.rf"
## [1] " indep_vars: biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.P.air, D.T.use, storage.fctr, D.P.mini, D.T.condition, D.npnct11.log, D.T.ipad, D.npnct10.log, D.sum.TfIdf, D.T.new, D.npnct13.log, D.npnct08.log, D.T.condit, color.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.nwrds.unq.log, D.ndgts.log, carrier.fctr, cellular.fctr, D.npnct14.log, prdline.my.fctr, D.npnct05.log, condition.fctr, startprice.log"## Loading required package: randomForest
## randomForest 4.6-10
## Type rfNews() to see new features/changes/bug fixes.
##
## Attaching package: 'randomForest'
##
## The following object is masked from 'package:dplyr':
##
## combine
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 36 on full training set
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 976 factor numeric
## err.rate 1500 -none- numeric
## confusion 6 -none- numeric
## votes 1952 matrix numeric
## oob.times 976 -none- numeric
## classes 2 -none- character
## importance 71 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 976 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 71 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320953
## 2 0.1 0.8170290
## 3 0.2 0.9213483
## 4 0.3 0.9698925
## 5 0.4 0.9922992
## 6 0.5 0.9911111
## 7 0.6 0.9831650
## 8 0.7 0.9402110
## 9 0.8 0.8513342
## 10 0.9 0.7338936
## 11 1.0 0.1286307
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.All.X.no.rnorm.rf.N
## 1 N 518
## 2 Y NA
## sold.fctr.predict.All.X.no.rnorm.rf.Y
## 1 7
## 2 451
## Prediction
## Reference N Y
## N 518 7
## Y 0 451
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.928279e-01 9.855886e-01 9.852788e-01 9.971117e-01 5.379098e-01
## AccuracyPValue McnemarPValue
## 8.483810e-247 2.334220e-02
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6321484
## 2 0.1 0.6638225
## 3 0.2 0.7116683
## 4 0.3 0.7510823
## 5 0.4 0.7582160
## 6 0.5 0.7731305
## 7 0.6 0.7540541
## 8 0.7 0.7306590
## 9 0.8 0.6921899
## 10 0.9 0.5915966
## 11 1.0 0.1451247
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.All.X.no.rnorm.rf.N
## 1 N 401
## 2 Y 104
## sold.fctr.predict.All.X.no.rnorm.rf.Y
## 1 75
## 2 305
## Prediction
## Reference N Y
## N 401 75
## Y 104 305
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.977401e-01 5.911080e-01 7.697388e-01 8.237428e-01 5.378531e-01
## AccuracyPValue McnemarPValue
## 1.113230e-58 3.636538e-02
## model_id model_method
## 1 All.X.no.rnorm.rf rf
## feats
## 1 biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.P.air, D.T.use, storage.fctr, D.P.mini, D.T.condition, D.npnct11.log, D.T.ipad, D.npnct10.log, D.sum.TfIdf, D.T.new, D.npnct13.log, D.npnct08.log, D.T.condit, color.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.nwrds.unq.log, D.ndgts.log, carrier.fctr, cellular.fctr, D.npnct14.log, prdline.my.fctr, D.npnct05.log, condition.fctr, startprice.log
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 16.265 4.432
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9989906 0.4 0.9922992 0.7889287
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.9852788 0.9971117 0.5713756 0.8503935
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.7731305 0.7977401
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7697388 0.8237428 0.591108
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.03570954 0.07271166# User specified
# Ensure at least 2 vars in each regression; else varImp crashes
# sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df; sav_featsimp_df <- glb_featsimp_df
# glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df; glm_featsimp_df <- sav_featsimp_df
# easier to exclude features
# require(gdata) # needed for trim
# model_id <- "";
# indep_vars_vctr <- head(subset(glb_models_df, grepl("All\\.X\\.", model_id), select=feats)
# , 1)[, "feats"]
# indep_vars_vctr <- trim(unlist(strsplit(indep_vars_vctr, "[,]")))
# indep_vars_vctr <- setdiff(indep_vars_vctr, ".rnorm")
# easier to include features
#model_id <- "Rank9.2"; indep_vars_vctr <- c(NULL
# ,"<feat1>"
# ,"<feat1>*<feat2>"
# ,"<feat1>:<feat2>"
# )
# for (method in c("bayesglm")) {
# ret_lst <- myfit_mdl(model_id=model_id, model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
# csm_mdl_id <- paste0(model_id, ".", method)
# csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(model_id, ".", method)]]); print(head(csm_featsimp_df))
# }
# Ntv.1.lm <- lm(reformulate(indep_vars_vctr, glb_rsp_var), glb_trnobs_df); print(summary(Ntv.1.lm))
#print(dsp_models_df <- orderBy(model_sel_frmla, glb_models_df)[, dsp_models_cols])
#csm_featsimp_df[grepl("H.npnct19.log", row.names(csm_featsimp_df)), , FALSE]
#csm_OOBobs_df <- glb_get_predictions(glb_OOBobs_df, mdl_id=csm_mdl_id, rsp_var_out=glb_rsp_var_out, prob_threshold_def=glb_models_df[glb_models_df$model_id == csm_mdl_id, "opt.prob.threshold.OOB"])
#print(sprintf("%s OOB confusion matrix & accuracy: ", csm_mdl_id)); print(t(confusionMatrix(csm_OOBobs_df[, paste0(glb_rsp_var_out, csm_mdl_id)], csm_OOBobs_df[, glb_rsp_var])$table))
#glb_models_df[, "max.Accuracy.OOB", FALSE]
#varImp(glb_models_lst[["Low.cor.X.glm"]])
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.2.glm"]])$importance)
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.3.glm"]])$importance)
#glb_feats_df[grepl("npnct28", glb_feats_df$id), ]
#print(sprintf("%s OOB confusion matrix & accuracy: ", glb_sel_mdl_id)); print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)], glb_OOBobs_df[, glb_rsp_var])$table))
# User specified bivariate models
# indep_vars_vctr_lst <- list()
# for (feat in setdiff(names(glb_fitobs_df),
# union(glb_rsp_var, glb_exclude_vars_as_features)))
# indep_vars_vctr_lst[["feat"]] <- feat
# User specified combinatorial models
# indep_vars_vctr_lst <- list()
# combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"),
# <num_feats_to_choose>)
# for (combn_ix in 1:ncol(combn_mtrx))
# #print(combn_mtrx[, combn_ix])
# indep_vars_vctr_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
# template for myfit_mdl
# rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
# only for OOB in trainControl ?
# ret_lst <- myfit_mdl_fn(model_id=paste0(model_id_pfx, ""), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df,
# model_loss_mtrx=glb_model_metric_terms,
# model_summaryFunction=glb_model_metric_smmry,
# model_metric=glb_model_metric,
# model_metric_maximize=glb_model_metric_maximize)
# Simplify a model
# fit_df <- glb_fitobs_df; glb_mdl <- step(<complex>_mdl)
# Non-caret models
# rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var),
# data=glb_fitobs_df, #method="class",
# control=rpart.control(cp=0.12),
# parms=list(loss=glb_model_metric_terms))
# print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
#
print(glb_models_df)## model_id model_method
## MFO.myMFO_classfr MFO.myMFO_classfr myMFO_classfr
## Random.myrandom_classfr Random.myrandom_classfr myrandom_classfr
## Max.cor.Y.cv.0.rpart Max.cor.Y.cv.0.rpart rpart
## Max.cor.Y.cv.0.cp.0.rpart Max.cor.Y.cv.0.cp.0.rpart rpart
## Max.cor.Y.rpart Max.cor.Y.rpart rpart
## Max.cor.Y.glm Max.cor.Y.glm glm
## Interact.High.cor.Y.glm Interact.High.cor.Y.glm glm
## Low.cor.X.glm Low.cor.X.glm glm
## All.X.glm All.X.glm glm
## All.X.bayesglm All.X.bayesglm bayesglm
## All.X.no.rnorm.rpart All.X.no.rnorm.rpart rpart
## All.X.no.rnorm.rf All.X.no.rnorm.rf rf
## feats
## MFO.myMFO_classfr .rnorm
## Random.myrandom_classfr .rnorm
## Max.cor.Y.cv.0.rpart biddable, startprice.log
## Max.cor.Y.cv.0.cp.0.rpart biddable, startprice.log
## Max.cor.Y.rpart biddable, startprice.log
## Max.cor.Y.glm biddable, startprice.log
## Interact.High.cor.Y.glm biddable, startprice.log, biddable:D.nwrds.unq.log, biddable:D.T.like, biddable:D.npnct06.log, biddable:D.ratio.nstopwrds.nwrds, biddable:D.nchrs.log, biddable:D.nwrds.log, biddable:cellular.fctr
## Low.cor.X.glm biddable, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.P.air, D.T.use, storage.fctr, D.P.mini, D.T.condition, D.npnct11.log, .rnorm, D.T.ipad, D.npnct10.log, D.npnct08.log, D.T.condit, color.fctr, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.npnct09.log, D.nwrds.unq.log, D.ndgts.log, cellular.fctr, D.npnct14.log, prdline.my.fctr, D.npnct05.log, condition.fctr, startprice.log
## All.X.glm biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.P.air, D.T.use, storage.fctr, D.P.mini, D.T.condition, D.npnct11.log, .rnorm, D.T.ipad, D.npnct10.log, D.sum.TfIdf, D.T.new, D.npnct13.log, D.npnct08.log, D.T.condit, color.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.nwrds.unq.log, D.ndgts.log, carrier.fctr, cellular.fctr, D.npnct14.log, prdline.my.fctr, D.npnct05.log, condition.fctr, startprice.log
## All.X.bayesglm biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.P.air, D.T.use, storage.fctr, D.P.mini, D.T.condition, D.npnct11.log, .rnorm, D.T.ipad, D.npnct10.log, D.sum.TfIdf, D.T.new, D.npnct13.log, D.npnct08.log, D.T.condit, color.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.nwrds.unq.log, D.ndgts.log, carrier.fctr, cellular.fctr, D.npnct14.log, prdline.my.fctr, D.npnct05.log, condition.fctr, startprice.log
## All.X.no.rnorm.rpart biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.P.air, D.T.use, storage.fctr, D.P.mini, D.T.condition, D.npnct11.log, D.T.ipad, D.npnct10.log, D.sum.TfIdf, D.T.new, D.npnct13.log, D.npnct08.log, D.T.condit, color.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.nwrds.unq.log, D.ndgts.log, carrier.fctr, cellular.fctr, D.npnct14.log, prdline.my.fctr, D.npnct05.log, condition.fctr, startprice.log
## All.X.no.rnorm.rf biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.P.air, D.T.use, storage.fctr, D.P.mini, D.T.condition, D.npnct11.log, D.T.ipad, D.npnct10.log, D.sum.TfIdf, D.T.new, D.npnct13.log, D.npnct08.log, D.T.condit, color.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.nwrds.unq.log, D.ndgts.log, carrier.fctr, cellular.fctr, D.npnct14.log, prdline.my.fctr, D.npnct05.log, condition.fctr, startprice.log
## max.nTuningRuns min.elapsedtime.everything
## MFO.myMFO_classfr 0 0.366
## Random.myrandom_classfr 0 0.253
## Max.cor.Y.cv.0.rpart 0 0.678
## Max.cor.Y.cv.0.cp.0.rpart 0 0.461
## Max.cor.Y.rpart 3 0.993
## Max.cor.Y.glm 1 0.976
## Interact.High.cor.Y.glm 1 0.988
## Low.cor.X.glm 1 1.247
## All.X.glm 1 1.356
## All.X.bayesglm 1 2.307
## All.X.no.rnorm.rpart 3 1.389
## All.X.no.rnorm.rf 3 16.265
## min.elapsedtime.final max.auc.fit
## MFO.myMFO_classfr 0.002 0.5000000
## Random.myrandom_classfr 0.001 0.5040735
## Max.cor.Y.cv.0.rpart 0.011 0.5000000
## Max.cor.Y.cv.0.cp.0.rpart 0.010 0.8769591
## Max.cor.Y.rpart 0.011 0.7639489
## Max.cor.Y.glm 0.013 0.8417506
## Interact.High.cor.Y.glm 0.016 0.8437145
## Low.cor.X.glm 0.128 0.8721233
## All.X.glm 0.164 0.8793749
## All.X.bayesglm 0.271 0.8773815
## All.X.no.rnorm.rpart 0.057 0.7639489
## All.X.no.rnorm.rf 4.432 0.9989906
## opt.prob.threshold.fit max.f.score.fit
## MFO.myMFO_classfr 0.5 0.0000000
## Random.myrandom_classfr 0.4 0.6320953
## Max.cor.Y.cv.0.rpart 0.5 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.4 0.7964602
## Max.cor.Y.rpart 0.7 0.7432432
## Max.cor.Y.glm 0.4 0.7513691
## Interact.High.cor.Y.glm 0.4 0.7546755
## Low.cor.X.glm 0.6 0.7921760
## All.X.glm 0.4 0.7887324
## All.X.bayesglm 0.6 0.7897311
## All.X.no.rnorm.rpart 0.7 0.7432432
## All.X.no.rnorm.rf 0.4 0.9922992
## max.Accuracy.fit max.AccuracyLower.fit
## MFO.myMFO_classfr 0.5379098 0.5060449
## Random.myrandom_classfr 0.4620902 0.4304545
## Max.cor.Y.cv.0.rpart 0.5379098 0.5060449
## Max.cor.Y.cv.0.cp.0.rpart 0.8114754 0.7854988
## Max.cor.Y.rpart 0.7786629 0.7385542
## Max.cor.Y.glm 0.7725185 0.7396162
## Interact.High.cor.Y.glm 0.7766305 0.7438660
## Low.cor.X.glm 0.7551046 0.8005408
## All.X.glm 0.7530628 0.7737180
## All.X.bayesglm 0.7571433 0.7983885
## All.X.no.rnorm.rpart 0.7786755 0.7385542
## All.X.no.rnorm.rf 0.7889287 0.9852788
## max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## MFO.myMFO_classfr 0.5695455 0.0000000 0.5000000
## Random.myrandom_classfr 0.4939551 0.0000000 0.5214656
## Max.cor.Y.cv.0.rpart 0.5695455 0.0000000 0.5000000
## Max.cor.Y.cv.0.cp.0.rpart 0.8355569 0.6208893 0.8257253
## Max.cor.Y.rpart 0.7926151 0.5507393 0.7844353
## Max.cor.Y.glm 0.7935960 0.5401437 0.8357056
## Interact.High.cor.Y.glm 0.7975178 0.5477252 0.8370000
## Low.cor.X.glm 0.8491145 0.5058720 0.8289074
## All.X.glm 0.8248664 0.5017193 0.8281780
## All.X.bayesglm 0.8471812 0.5093883 0.8305151
## All.X.no.rnorm.rpart 0.7926151 0.5490774 0.7844353
## All.X.no.rnorm.rf 0.9971117 0.5713756 0.8503935
## opt.prob.threshold.OOB max.f.score.OOB
## MFO.myMFO_classfr 0.5 0.0000000
## Random.myrandom_classfr 0.4 0.6321484
## Max.cor.Y.cv.0.rpart 0.5 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.5 0.7515763
## Max.cor.Y.rpart 0.7 0.7663782
## Max.cor.Y.glm 0.5 0.7674714
## Interact.High.cor.Y.glm 0.4 0.7651332
## Low.cor.X.glm 0.5 0.7650834
## All.X.glm 0.5 0.7569620
## All.X.bayesglm 0.5 0.7649301
## All.X.no.rnorm.rpart 0.7 0.7663782
## All.X.no.rnorm.rf 0.5 0.7731305
## max.Accuracy.OOB max.AccuracyLower.OOB
## MFO.myMFO_classfr 0.5378531 0.5043611
## Random.myrandom_classfr 0.4621469 0.4289077
## Max.cor.Y.cv.0.rpart 0.5378531 0.5043611
## Max.cor.Y.cv.0.cp.0.rpart 0.7774011 0.7485294
## Max.cor.Y.rpart 0.7864407 0.7579436
## Max.cor.Y.glm 0.7932203 0.7650169
## Interact.High.cor.Y.glm 0.7807910 0.7520575
## Low.cor.X.glm 0.7932203 0.7650169
## All.X.glm 0.7830508 0.7544110
## All.X.bayesglm 0.7909605 0.7626579
## All.X.no.rnorm.rpart 0.7864407 0.7579436
## All.X.no.rnorm.rf 0.7977401 0.7697388
## max.AccuracyUpper.OOB max.Kappa.OOB
## MFO.myMFO_classfr 0.5710923 0.0000000
## Random.myrandom_classfr 0.4956389 0.0000000
## Max.cor.Y.cv.0.rpart 0.5710923 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.8044124 0.5503014
## Max.cor.Y.rpart 0.8130160 0.5697529
## Max.cor.Y.glm 0.8194558 0.5818261
## Interact.High.cor.Y.glm 0.8076410 0.5596608
## Low.cor.X.glm 0.8194558 0.5812465
## All.X.glm 0.8097919 0.5614877
## All.X.bayesglm 0.8173104 0.5772559
## All.X.no.rnorm.rpart 0.8130160 0.5697529
## All.X.no.rnorm.rf 0.8237428 0.5911080
## max.AccuracySD.fit max.KappaSD.fit min.aic.fit
## MFO.myMFO_classfr NA NA NA
## Random.myrandom_classfr NA NA NA
## Max.cor.Y.cv.0.rpart NA NA NA
## Max.cor.Y.cv.0.cp.0.rpart NA NA NA
## Max.cor.Y.rpart 0.04549097 0.09113973 NA
## Max.cor.Y.glm 0.03487292 0.07025074 942.0369
## Interact.High.cor.Y.glm 0.02572891 0.05220556 950.5986
## Low.cor.X.glm 0.01887657 0.03908066 963.5142
## All.X.glm 0.01797809 0.03832757 976.6992
## All.X.bayesglm 0.03574371 0.07291062 982.9554
## All.X.no.rnorm.rpart 0.03268626 0.06824050 NA
## All.X.no.rnorm.rf 0.03570954 0.07271166 NArm(ret_lst)
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df, "fit.models_1_end",
major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 5 fit.models_1_rf 5 0 88.996 109.134 20.138
## 6 fit.models_1_end 6 0 109.135 NA NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 11 fit.models 7 1 68.916 109.142 40.226
## 12 fit.models 7 2 109.143 NA NAif (!is.null(glb_model_metric_smmry)) {
stats_df <- glb_models_df[, "model_id", FALSE]
stats_mdl_df <- data.frame()
for (model_id in stats_df$model_id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[model_id]], glb_fitobs_df, glb_rsp_var,
glb_rsp_var_out, model_id, "fit",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
stats_mdl_df <- data.frame()
for (model_id in stats_df$model_id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[model_id]], glb_OOBobs_df, glb_rsp_var,
glb_rsp_var_out, model_id, "OOB",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
print("Merging following data into glb_models_df:")
print(stats_mrg_df <- stats_df[, c(1, grep(glb_model_metric, names(stats_df)))])
print(tmp_models_df <- orderBy(~model_id, glb_models_df[, c("model_id",
grep(glb_model_metric, names(stats_df), value=TRUE))]))
tmp2_models_df <- glb_models_df[, c("model_id", setdiff(names(glb_models_df),
grep(glb_model_metric, names(stats_df), value=TRUE)))]
tmp3_models_df <- merge(tmp2_models_df, stats_mrg_df, all.x=TRUE, sort=FALSE)
print(tmp3_models_df)
print(names(tmp3_models_df))
print(glb_models_df <- subset(tmp3_models_df, select=-model_id.1))
}
plt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
plt_models_df[, sub("min.", "inv.", var)] <-
#ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
1.0 / plt_models_df[, var]
plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)## model_id model_method
## MFO.myMFO_classfr MFO.myMFO_classfr myMFO_classfr
## Random.myrandom_classfr Random.myrandom_classfr myrandom_classfr
## Max.cor.Y.cv.0.rpart Max.cor.Y.cv.0.rpart rpart
## Max.cor.Y.cv.0.cp.0.rpart Max.cor.Y.cv.0.cp.0.rpart rpart
## Max.cor.Y.rpart Max.cor.Y.rpart rpart
## Max.cor.Y.glm Max.cor.Y.glm glm
## Interact.High.cor.Y.glm Interact.High.cor.Y.glm glm
## Low.cor.X.glm Low.cor.X.glm glm
## All.X.glm All.X.glm glm
## All.X.bayesglm All.X.bayesglm bayesglm
## All.X.no.rnorm.rpart All.X.no.rnorm.rpart rpart
## All.X.no.rnorm.rf All.X.no.rnorm.rf rf
## feats
## MFO.myMFO_classfr .rnorm
## Random.myrandom_classfr .rnorm
## Max.cor.Y.cv.0.rpart biddable, startprice.log
## Max.cor.Y.cv.0.cp.0.rpart biddable, startprice.log
## Max.cor.Y.rpart biddable, startprice.log
## Max.cor.Y.glm biddable, startprice.log
## Interact.High.cor.Y.glm biddable, startprice.log, biddable:D.nwrds.unq.log, biddable:D.T.like, biddable:D.npnct06.log, biddable:D.ratio.nstopwrds.nwrds, biddable:D.nchrs.log, biddable:D.nwrds.log, biddable:cellular.fctr
## Low.cor.X.glm biddable, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.P.air, D.T.use, storage.fctr, D.P.mini, D.T.condition, D.npnct11.log, .rnorm, D.T.ipad, D.npnct10.log, D.npnct08.log, D.T.condit, color.fctr, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.npnct09.log, D.nwrds.unq.log, D.ndgts.log, cellular.fctr, D.npnct14.log, prdline.my.fctr, D.npnct05.log, condition.fctr, startprice.log
## All.X.glm biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.P.air, D.T.use, storage.fctr, D.P.mini, D.T.condition, D.npnct11.log, .rnorm, D.T.ipad, D.npnct10.log, D.sum.TfIdf, D.T.new, D.npnct13.log, D.npnct08.log, D.T.condit, color.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.nwrds.unq.log, D.ndgts.log, carrier.fctr, cellular.fctr, D.npnct14.log, prdline.my.fctr, D.npnct05.log, condition.fctr, startprice.log
## All.X.bayesglm biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.P.air, D.T.use, storage.fctr, D.P.mini, D.T.condition, D.npnct11.log, .rnorm, D.T.ipad, D.npnct10.log, D.sum.TfIdf, D.T.new, D.npnct13.log, D.npnct08.log, D.T.condit, color.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.nwrds.unq.log, D.ndgts.log, carrier.fctr, cellular.fctr, D.npnct14.log, prdline.my.fctr, D.npnct05.log, condition.fctr, startprice.log
## All.X.no.rnorm.rpart biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.P.air, D.T.use, storage.fctr, D.P.mini, D.T.condition, D.npnct11.log, D.T.ipad, D.npnct10.log, D.sum.TfIdf, D.T.new, D.npnct13.log, D.npnct08.log, D.T.condit, color.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.nwrds.unq.log, D.ndgts.log, carrier.fctr, cellular.fctr, D.npnct14.log, prdline.my.fctr, D.npnct05.log, condition.fctr, startprice.log
## All.X.no.rnorm.rf biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.P.air, D.T.use, storage.fctr, D.P.mini, D.T.condition, D.npnct11.log, D.T.ipad, D.npnct10.log, D.sum.TfIdf, D.T.new, D.npnct13.log, D.npnct08.log, D.T.condit, color.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.nwrds.unq.log, D.ndgts.log, carrier.fctr, cellular.fctr, D.npnct14.log, prdline.my.fctr, D.npnct05.log, condition.fctr, startprice.log
## max.nTuningRuns max.auc.fit
## MFO.myMFO_classfr 0 0.5000000
## Random.myrandom_classfr 0 0.5040735
## Max.cor.Y.cv.0.rpart 0 0.5000000
## Max.cor.Y.cv.0.cp.0.rpart 0 0.8769591
## Max.cor.Y.rpart 3 0.7639489
## Max.cor.Y.glm 1 0.8417506
## Interact.High.cor.Y.glm 1 0.8437145
## Low.cor.X.glm 1 0.8721233
## All.X.glm 1 0.8793749
## All.X.bayesglm 1 0.8773815
## All.X.no.rnorm.rpart 3 0.7639489
## All.X.no.rnorm.rf 3 0.9989906
## opt.prob.threshold.fit max.f.score.fit
## MFO.myMFO_classfr 0.5 0.0000000
## Random.myrandom_classfr 0.4 0.6320953
## Max.cor.Y.cv.0.rpart 0.5 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.4 0.7964602
## Max.cor.Y.rpart 0.7 0.7432432
## Max.cor.Y.glm 0.4 0.7513691
## Interact.High.cor.Y.glm 0.4 0.7546755
## Low.cor.X.glm 0.6 0.7921760
## All.X.glm 0.4 0.7887324
## All.X.bayesglm 0.6 0.7897311
## All.X.no.rnorm.rpart 0.7 0.7432432
## All.X.no.rnorm.rf 0.4 0.9922992
## max.Accuracy.fit max.Kappa.fit max.auc.OOB
## MFO.myMFO_classfr 0.5379098 0.0000000 0.5000000
## Random.myrandom_classfr 0.4620902 0.0000000 0.5214656
## Max.cor.Y.cv.0.rpart 0.5379098 0.0000000 0.5000000
## Max.cor.Y.cv.0.cp.0.rpart 0.8114754 0.6208893 0.8257253
## Max.cor.Y.rpart 0.7786629 0.5507393 0.7844353
## Max.cor.Y.glm 0.7725185 0.5401437 0.8357056
## Interact.High.cor.Y.glm 0.7766305 0.5477252 0.8370000
## Low.cor.X.glm 0.7551046 0.5058720 0.8289074
## All.X.glm 0.7530628 0.5017193 0.8281780
## All.X.bayesglm 0.7571433 0.5093883 0.8305151
## All.X.no.rnorm.rpart 0.7786755 0.5490774 0.7844353
## All.X.no.rnorm.rf 0.7889287 0.5713756 0.8503935
## opt.prob.threshold.OOB max.f.score.OOB
## MFO.myMFO_classfr 0.5 0.0000000
## Random.myrandom_classfr 0.4 0.6321484
## Max.cor.Y.cv.0.rpart 0.5 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.5 0.7515763
## Max.cor.Y.rpart 0.7 0.7663782
## Max.cor.Y.glm 0.5 0.7674714
## Interact.High.cor.Y.glm 0.4 0.7651332
## Low.cor.X.glm 0.5 0.7650834
## All.X.glm 0.5 0.7569620
## All.X.bayesglm 0.5 0.7649301
## All.X.no.rnorm.rpart 0.7 0.7663782
## All.X.no.rnorm.rf 0.5 0.7731305
## max.Accuracy.OOB max.Kappa.OOB
## MFO.myMFO_classfr 0.5378531 0.0000000
## Random.myrandom_classfr 0.4621469 0.0000000
## Max.cor.Y.cv.0.rpart 0.5378531 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.7774011 0.5503014
## Max.cor.Y.rpart 0.7864407 0.5697529
## Max.cor.Y.glm 0.7932203 0.5818261
## Interact.High.cor.Y.glm 0.7807910 0.5596608
## Low.cor.X.glm 0.7932203 0.5812465
## All.X.glm 0.7830508 0.5614877
## All.X.bayesglm 0.7909605 0.5772559
## All.X.no.rnorm.rpart 0.7864407 0.5697529
## All.X.no.rnorm.rf 0.7977401 0.5911080
## inv.elapsedtime.everything inv.elapsedtime.final
## MFO.myMFO_classfr 2.73224044 500.0000000
## Random.myrandom_classfr 3.95256917 1000.0000000
## Max.cor.Y.cv.0.rpart 1.47492625 90.9090909
## Max.cor.Y.cv.0.cp.0.rpart 2.16919740 100.0000000
## Max.cor.Y.rpart 1.00704935 90.9090909
## Max.cor.Y.glm 1.02459016 76.9230769
## Interact.High.cor.Y.glm 1.01214575 62.5000000
## Low.cor.X.glm 0.80192462 7.8125000
## All.X.glm 0.73746313 6.0975610
## All.X.bayesglm 0.43346337 3.6900369
## All.X.no.rnorm.rpart 0.71994240 17.5438596
## All.X.no.rnorm.rf 0.06148171 0.2256318
## inv.aic.fit
## MFO.myMFO_classfr NA
## Random.myrandom_classfr NA
## Max.cor.Y.cv.0.rpart NA
## Max.cor.Y.cv.0.cp.0.rpart NA
## Max.cor.Y.rpart NA
## Max.cor.Y.glm 0.001061530
## Interact.High.cor.Y.glm 0.001051969
## Low.cor.X.glm 0.001037867
## All.X.glm 0.001023857
## All.X.bayesglm 0.001017340
## All.X.no.rnorm.rpart NA
## All.X.no.rnorm.rf NAprint(myplot_radar(radar_inp_df=plt_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 12. Consider specifying shapes manually if you must have them.## Warning: Removed 4 rows containing missing values (geom_path).## Warning: Removed 87 rows containing missing values (geom_point).## Warning: Removed 7 rows containing missing values (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 12. Consider specifying shapes manually if you must have them.
# print(myplot_radar(radar_inp_df=subset(plt_models_df,
# !(model_id %in% grep("random|MFO", plt_models_df$model_id, value=TRUE)))))
# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df,
max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
# Does CI alredy exist ?
var_components <- unlist(strsplit(var, "SD"))
varActul <- paste0(var_components[1], var_components[2])
varUpper <- paste0(var_components[1], "Upper", var_components[2])
varLower <- paste0(var_components[1], "Lower", var_components[2])
if (varUpper %in% names(glb_models_df)) {
warning(varUpper, " already exists in glb_models_df")
# Assuming Lower also exists
next
}
print(sprintf("var:%s", var))
# CI is dependent on sample size in t distribution; df=n-1
glb_models_df[, varUpper] <- glb_models_df[, varActul] +
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
glb_models_df[, varLower] <- glb_models_df[, varActul] -
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}## Warning: max.AccuracyUpper.fit already exists in glb_models_df## [1] "var:max.KappaSD.fit"# Plot metrics with CI
plt_models_df <- glb_models_df[, "model_id", FALSE]
pltCI_models_df <- glb_models_df[, "model_id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
var_components <- unlist(strsplit(var, "Upper"))
col_name <- unlist(paste(var_components, collapse=""))
plt_models_df[, col_name] <- glb_models_df[, col_name]
for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
pltCI_models_df[, name] <- glb_models_df[, name]
}
build_statsCI_data <- function(plt_models_df) {
mltd_models_df <- melt(plt_models_df, id.vars="model_id")
mltd_models_df$data <- sapply(1:nrow(mltd_models_df),
function(row_ix) tail(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]), "[.]")), 1))
mltd_models_df$label <- sapply(1:nrow(mltd_models_df),
function(row_ix) head(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]),
paste0(".", mltd_models_df[row_ix, "data"]))), 1))
#print(mltd_models_df)
return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)
mltdCI_models_df <- melt(pltCI_models_df, id.vars="model_id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
for (type in c("Upper", "Lower")) {
if (length(var_components <- unlist(strsplit(
as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
#print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
mltdCI_models_df[row_ix, "label"] <- var_components[1]
mltdCI_models_df[row_ix, "data"] <-
unlist(strsplit(var_components[2], "[.]"))[2]
mltdCI_models_df[row_ix, "type"] <- type
break
}
}
}
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable),
timevar="type",
idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")),
direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)
# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
for (type in unique(mltd_models_df$data)) {
var_type <- paste0(var, ".", type)
# if this data is already present, next
if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
sep=".")))
next
#print(sprintf("var_type:%s", var_type))
goback_vars <- c(goback_vars, var_type)
}
}
if (length(goback_vars) > 0) {
mltd_goback_df <- build_statsCI_data(glb_models_df[, c("model_id", goback_vars)])
mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}
mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("model_id", "model_method")],
all.x=TRUE)
png(paste0(glb_out_pfx, "models_bar.png"), width=480*3, height=480*2)
print(gp <- myplot_bar(mltd_models_df, "model_id", "value", colorcol_name="model_method") +
geom_errorbar(data=mrgdCI_models_df,
mapping=aes(x=model_id, ymax=value.Upper, ymin=value.Lower), width=0.5) +
facet_grid(label ~ data, scales="free") +
theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))
dev.off()## quartz_off_screen
## 2print(gp)
# used for console inspection
model_evl_terms <- c(NULL)
for (metric in glb_model_evl_criteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
if (glb_is_classification && glb_is_binomial)
model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse=" "))
dsp_models_cols <- c("model_id", glb_model_evl_criteria)
if (glb_is_classification && glb_is_binomial)
dsp_models_cols <- c(dsp_models_cols, "opt.prob.threshold.OOB")
print(dsp_models_df <- orderBy(model_sel_frmla, glb_models_df)[, dsp_models_cols])## model_id max.Accuracy.OOB max.auc.OOB max.Kappa.OOB
## 12 All.X.no.rnorm.rf 0.7977401 0.8503935 0.5911080
## 6 Max.cor.Y.glm 0.7932203 0.8357056 0.5818261
## 8 Low.cor.X.glm 0.7932203 0.8289074 0.5812465
## 10 All.X.bayesglm 0.7909605 0.8305151 0.5772559
## 5 Max.cor.Y.rpart 0.7864407 0.7844353 0.5697529
## 11 All.X.no.rnorm.rpart 0.7864407 0.7844353 0.5697529
## 9 All.X.glm 0.7830508 0.8281780 0.5614877
## 7 Interact.High.cor.Y.glm 0.7807910 0.8370000 0.5596608
## 4 Max.cor.Y.cv.0.cp.0.rpart 0.7774011 0.8257253 0.5503014
## 1 MFO.myMFO_classfr 0.5378531 0.5000000 0.0000000
## 3 Max.cor.Y.cv.0.rpart 0.5378531 0.5000000 0.0000000
## 2 Random.myrandom_classfr 0.4621469 0.5214656 0.0000000
## min.aic.fit opt.prob.threshold.OOB
## 12 NA 0.5
## 6 942.0369 0.5
## 8 963.5142 0.5
## 10 982.9554 0.5
## 5 NA 0.7
## 11 NA 0.7
## 9 976.6992 0.5
## 7 950.5986 0.4
## 4 NA 0.5
## 1 NA 0.5
## 3 NA 0.5
## 2 NA 0.4print(myplot_radar(radar_inp_df=dsp_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 12. Consider specifying shapes manually if you must have them.## Warning: Removed 38 rows containing missing values (geom_point).## Warning: Removed 7 rows containing missing values (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 12. Consider specifying shapes manually if you must have them.
print("Metrics used for model selection:"); print(model_sel_frmla)## [1] "Metrics used for model selection:"## ~-max.Accuracy.OOB - max.auc.OOB - max.Kappa.OOB + min.aic.fit -
## opt.prob.threshold.OOBprint(sprintf("Best model id: %s", dsp_models_df[1, "model_id"]))## [1] "Best model id: All.X.no.rnorm.rf"if (is.null(glb_sel_mdl_id)) {
glb_sel_mdl_id <- dsp_models_df[1, "model_id"]
# if (glb_sel_mdl_id == "Interact.High.cor.Y.glm") {
# warning("glb_sel_mdl_id: Interact.High.cor.Y.glm; myextract_mdl_feats does not currently support interaction terms")
# glb_sel_mdl_id <- dsp_models_df[2, "model_id"]
# }
} else
print(sprintf("User specified selection: %s", glb_sel_mdl_id))
myprint_mdl(glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]])
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 976 factor numeric
## err.rate 1500 -none- numeric
## confusion 6 -none- numeric
## votes 1952 matrix numeric
## oob.times 976 -none- numeric
## classes 2 -none- character
## importance 71 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 976 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 71 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character## [1] TRUE# From here to save(), this should all be in one function
# these are executed in the same seq twice more:
# fit.data.training & predict.data.new chunks
glb_get_predictions <- function(df, mdl_id, rsp_var_out, prob_threshold_def=NULL) {
mdl <- glb_models_lst[[mdl_id]]
rsp_var_out <- paste0(rsp_var_out, mdl_id)
if (glb_is_regression) {
df[, rsp_var_out] <- predict(mdl, newdata=df, type="raw")
print(myplot_scatter(df, glb_rsp_var, rsp_var_out, smooth=TRUE))
df[, paste0(rsp_var_out, ".err")] <-
abs(df[, rsp_var_out] - df[, glb_rsp_var])
print(head(orderBy(reformulate(c("-", paste0(rsp_var_out, ".err"))),
df)))
}
if (glb_is_classification && glb_is_binomial) {
prob_threshold <- glb_models_df[glb_models_df$model_id == mdl_id,
"opt.prob.threshold.OOB"]
if (is.null(prob_threshold) || is.na(prob_threshold)) {
warning("Using default probability threshold: ", prob_threshold_def)
if (is.null(prob_threshold <- prob_threshold_def))
stop("Default probability threshold is NULL")
}
df[, paste0(rsp_var_out, ".prob")] <-
predict(mdl, newdata=df, type="prob")[, 2]
df[, rsp_var_out] <-
factor(levels(df[, glb_rsp_var])[
(df[, paste0(rsp_var_out, ".prob")] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# prediction stats already reported by myfit_mdl ???
}
if (glb_is_classification && !glb_is_binomial) {
df[, rsp_var_out] <- predict(mdl, newdata=df, type="raw")
df[, paste0(rsp_var_out, ".prob")] <-
predict(mdl, newdata=df, type="prob")
}
return(df)
}
glb_OOBobs_df <- glb_get_predictions(df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id,
rsp_var_out=glb_rsp_var_out)
predct_accurate_var_name <- paste0(glb_rsp_var_out, glb_sel_mdl_id, ".accurate")
glb_OOBobs_df[, predct_accurate_var_name] <-
(glb_OOBobs_df[, glb_rsp_var] ==
glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)])
glb_featsimp_df <-
myget_feats_importance(mdl=glb_sel_mdl, featsimp_df=NULL)
glb_featsimp_df[, paste0(glb_sel_mdl_id, ".importance")] <- glb_featsimp_df$importance
print(glb_featsimp_df)## importance
## startprice.log 1.000000e+02
## biddable 6.585711e+01
## prdline.my.fctriPadAir 7.742557e+00
## D.sum.TfIdf 6.599497e+00
## D.ratio.sum.TfIdf.nwrds 6.017068e+00
## D.ratio.nstopwrds.nwrds 5.141973e+00
## condition.fctrFor parts or not working 5.031585e+00
## prdline.my.fctriPad 3+ 5.017083e+00
## color.fctrBlack 4.674836e+00
## color.fctrWhite 4.566188e+00
## prdline.my.fctriPadmini 4.262759e+00
## condition.fctrNew 4.081109e+00
## storage.fctr16 4.035228e+00
## color.fctrSpace Gray 3.723290e+00
## prdline.my.fctrUnknown 3.666784e+00
## storage.fctr64 3.647991e+00
## cellular.fctr1 3.542109e+00
## storage.fctr32 3.473306e+00
## D.nchrs.log 3.322331e+00
## prdline.my.fctriPad 2 3.313279e+00
## D.nuppr.log 3.298557e+00
## D.nwrds.unq.log 3.111615e+00
## D.nwrds.log 2.820643e+00
## carrier.fctrUnknown 2.697432e+00
## D.nstopwrds.log 2.682286e+00
## carrier.fctrAT&T 2.494405e+00
## prdline.my.fctriPadmini 2+ 2.492035e+00
## cellular.fctrUnknown 2.330567e+00
## carrier.fctrVerizon 2.284560e+00
## D.T.use 2.012339e+00
## storage.fctrUnknown 2.006601e+00
## condition.fctrSeller refurbished 1.961753e+00
## D.npnct13.log 1.895144e+00
## D.T.condition 1.870207e+00
## condition.fctrNew other (see details) 1.864832e+00
## color.fctrGold 1.764521e+00
## D.T.veri 1.622926e+00
## D.T.scratch 1.492880e+00
## D.npnct11.log 1.456955e+00
## D.T.ipad 1.298498e+00
## D.T.work 1.256848e+00
## D.T.condit 1.181293e+00
## D.T.good 1.179454e+00
## D.T.great 1.092352e+00
## D.T.screen 1.078978e+00
## carrier.fctrT-Mobile 9.054299e-01
## D.npnct15.log 7.862747e-01
## condition.fctrManufacturer refurbished 7.709178e-01
## D.T.box 7.567341e-01
## D.T.this 7.472430e-01
## D.ndgts.log 7.008568e-01
## D.npnct14.log 6.508058e-01
## carrier.fctrSprint 6.205520e-01
## D.npnct24.log 5.812858e-01
## D.T.new 5.602444e-01
## D.npnct10.log 5.572065e-01
## D.T.used 5.217063e-01
## D.T.like 4.947486e-01
## D.T.excellent 4.858415e-01
## D.npnct08.log 4.418547e-01
## D.npnct16.log 3.276553e-01
## D.npnct01.log 2.657222e-01
## D.npnct12.log 2.524313e-01
## D.npnct03.log 2.476569e-01
## D.npnct05.log 2.422318e-01
## D.npnct06.log 1.910556e-01
## carrier.fctrOther 8.536654e-02
## D.P.mini 6.911667e-02
## D.P.air 3.743295e-02
## D.npnct28.log 2.020533e-03
## D.npnct09.log 0.000000e+00
## All.X.no.rnorm.rf.importance
## startprice.log 1.000000e+02
## biddable 6.585711e+01
## prdline.my.fctriPadAir 7.742557e+00
## D.sum.TfIdf 6.599497e+00
## D.ratio.sum.TfIdf.nwrds 6.017068e+00
## D.ratio.nstopwrds.nwrds 5.141973e+00
## condition.fctrFor parts or not working 5.031585e+00
## prdline.my.fctriPad 3+ 5.017083e+00
## color.fctrBlack 4.674836e+00
## color.fctrWhite 4.566188e+00
## prdline.my.fctriPadmini 4.262759e+00
## condition.fctrNew 4.081109e+00
## storage.fctr16 4.035228e+00
## color.fctrSpace Gray 3.723290e+00
## prdline.my.fctrUnknown 3.666784e+00
## storage.fctr64 3.647991e+00
## cellular.fctr1 3.542109e+00
## storage.fctr32 3.473306e+00
## D.nchrs.log 3.322331e+00
## prdline.my.fctriPad 2 3.313279e+00
## D.nuppr.log 3.298557e+00
## D.nwrds.unq.log 3.111615e+00
## D.nwrds.log 2.820643e+00
## carrier.fctrUnknown 2.697432e+00
## D.nstopwrds.log 2.682286e+00
## carrier.fctrAT&T 2.494405e+00
## prdline.my.fctriPadmini 2+ 2.492035e+00
## cellular.fctrUnknown 2.330567e+00
## carrier.fctrVerizon 2.284560e+00
## D.T.use 2.012339e+00
## storage.fctrUnknown 2.006601e+00
## condition.fctrSeller refurbished 1.961753e+00
## D.npnct13.log 1.895144e+00
## D.T.condition 1.870207e+00
## condition.fctrNew other (see details) 1.864832e+00
## color.fctrGold 1.764521e+00
## D.T.veri 1.622926e+00
## D.T.scratch 1.492880e+00
## D.npnct11.log 1.456955e+00
## D.T.ipad 1.298498e+00
## D.T.work 1.256848e+00
## D.T.condit 1.181293e+00
## D.T.good 1.179454e+00
## D.T.great 1.092352e+00
## D.T.screen 1.078978e+00
## carrier.fctrT-Mobile 9.054299e-01
## D.npnct15.log 7.862747e-01
## condition.fctrManufacturer refurbished 7.709178e-01
## D.T.box 7.567341e-01
## D.T.this 7.472430e-01
## D.ndgts.log 7.008568e-01
## D.npnct14.log 6.508058e-01
## carrier.fctrSprint 6.205520e-01
## D.npnct24.log 5.812858e-01
## D.T.new 5.602444e-01
## D.npnct10.log 5.572065e-01
## D.T.used 5.217063e-01
## D.T.like 4.947486e-01
## D.T.excellent 4.858415e-01
## D.npnct08.log 4.418547e-01
## D.npnct16.log 3.276553e-01
## D.npnct01.log 2.657222e-01
## D.npnct12.log 2.524313e-01
## D.npnct03.log 2.476569e-01
## D.npnct05.log 2.422318e-01
## D.npnct06.log 1.910556e-01
## carrier.fctrOther 8.536654e-02
## D.P.mini 6.911667e-02
## D.P.air 3.743295e-02
## D.npnct28.log 2.020533e-03
## D.npnct09.log 0.000000e+00# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
featsimp_df <- glb_featsimp_df
featsimp_df$feat <- gsub("`(.*?)`", "\\1", row.names(featsimp_df))
featsimp_df$feat.interact <- gsub("(.*?):(.*)", "\\2", featsimp_df$feat)
featsimp_df$feat <- gsub("(.*?):(.*)", "\\1", featsimp_df$feat)
featsimp_df$feat.interact <- ifelse(featsimp_df$feat.interact == featsimp_df$feat,
NA, featsimp_df$feat.interact)
featsimp_df$feat <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat)
featsimp_df$feat.interact <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat.interact)
featsimp_df <- orderBy(~ -importance.max, summaryBy(importance ~ feat + feat.interact,
data=featsimp_df, FUN=max))
#rex_str=":(.*)"; txt_vctr=tail(featsimp_df$feat); ret_lst <- regexec(rex_str, txt_vctr); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
if (nrow(featsimp_df) > 5) {
warning("Limiting important feature scatter plots to 5 out of ", nrow(featsimp_df))
featsimp_df <- head(featsimp_df, 5)
}
# if (!all(is.na(featsimp_df$feat.interact)))
# stop("not implemented yet")
rsp_var_out <- paste0(glb_rsp_var_out, mdl_id)
for (var in featsimp_df$feat) {
plot_df <- melt(obs_df, id.vars=var,
measure.vars=c(glb_rsp_var, rsp_var_out))
# if (var == "<feat_name>") print(myplot_scatter(plot_df, var, "value",
# facet_colcol_name="variable") +
# geom_vline(xintercept=<divider_val>, linetype="dotted")) else
print(myplot_scatter(plot_df, var, "value", colorcol_name="variable",
facet_colcol_name="variable", jitter=TRUE) +
guides(color=FALSE))
}
if (glb_is_regression) {
if (nrow(featsimp_df) == 0)
warning("No important features in glb_fin_mdl") else
print(myplot_prediction_regression(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
id_vars=glb_id_var)
# + facet_wrap(reformulate(featsimp_df$feat[2])) # if [1 or 2] is a factor
# + geom_point(aes_string(color="<col_name>.fctr")) # to color the plot
)
}
if (glb_is_classification) {
if (nrow(featsimp_df) == 0)
warning("No features in selected model are statistically important")
else print(myplot_prediction_classification(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var,
rsp_var_out=rsp_var_out,
id_vars=glb_id_var,
prob_threshold=prob_threshold)
# + geom_hline(yintercept=<divider_val>, linetype = "dotted")
)
}
}
if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_OOBobs_df, mdl_id =
## glb_sel_mdl_id, : Limiting important feature scatter plots to 5 out of 50
## [1] "Min/Max Boundaries: "
## UniqueID sold.fctr sold.fctr.predict.All.X.no.rnorm.rf.prob
## 20 10020 N 0.122
## 182 10182 N 0.470
## 801 10801 N 0.500
## 1397 11397 N 0.044
## 285 10285 Y 0.938
## 1401 11401 N 0.508
## 512 10512 N 0.536
## 107 10107 N 0.568
## 1007 11007 N 0.632
## 1718 11718 N 0.638
## 841 10841 N 0.702
## 526 10526 N 0.738
## 941 10941 N 0.772
## 217 10217 N 0.788
## 1560 11560 N 0.798
## 1622 11622 N 0.958
## sold.fctr.predict.All.X.no.rnorm.rf
## 20 N
## 182 N
## 801 Y
## 1397 N
## 285 Y
## 1401 Y
## 512 Y
## 107 Y
## 1007 Y
## 1718 Y
## 841 Y
## 526 Y
## 941 Y
## 217 Y
## 1560 Y
## 1622 Y
## sold.fctr.predict.All.X.no.rnorm.rf.accurate
## 20 TRUE
## 182 TRUE
## 801 FALSE
## 1397 TRUE
## 285 TRUE
## 1401 FALSE
## 512 FALSE
## 107 FALSE
## 1007 FALSE
## 1718 FALSE
## 841 FALSE
## 526 FALSE
## 941 FALSE
## 217 FALSE
## 1560 FALSE
## 1622 FALSE
## sold.fctr.predict.All.X.no.rnorm.rf.error .label
## 20 0.000 10020
## 182 0.000 10182
## 801 0.000 10801
## 1397 0.000 11397
## 285 0.000 10285
## 1401 0.008 11401
## 512 0.036 10512
## 107 0.068 10107
## 1007 0.132 11007
## 1718 0.138 11718
## 841 0.202 10841
## 526 0.238 10526
## 941 0.272 10941
## 217 0.288 10217
## 1560 0.298 11560
## 1622 0.458 11622
## [1] "Inaccurate: "
## UniqueID sold.fctr sold.fctr.predict.All.X.no.rnorm.rf.prob
## 732 10732 Y 0.002
## 1540 11540 Y 0.008
## 30 10030 Y 0.032
## 436 10436 Y 0.034
## 1590 11590 Y 0.034
## 353 10353 Y 0.038
## sold.fctr.predict.All.X.no.rnorm.rf
## 732 N
## 1540 N
## 30 N
## 436 N
## 1590 N
## 353 N
## sold.fctr.predict.All.X.no.rnorm.rf.accurate
## 732 FALSE
## 1540 FALSE
## 30 FALSE
## 436 FALSE
## 1590 FALSE
## 353 FALSE
## sold.fctr.predict.All.X.no.rnorm.rf.error
## 732 -0.498
## 1540 -0.492
## 30 -0.468
## 436 -0.466
## 1590 -0.466
## 353 -0.462
## UniqueID sold.fctr sold.fctr.predict.All.X.no.rnorm.rf.prob
## 848 10848 Y 0.432
## 1648 11648 Y 0.438
## 1007 11007 N 0.632
## 1746 11746 N 0.640
## 1560 11560 N 0.798
## 595 10595 N 0.960
## sold.fctr.predict.All.X.no.rnorm.rf
## 848 N
## 1648 N
## 1007 Y
## 1746 Y
## 1560 Y
## 595 Y
## sold.fctr.predict.All.X.no.rnorm.rf.accurate
## 848 FALSE
## 1648 FALSE
## 1007 FALSE
## 1746 FALSE
## 1560 FALSE
## 595 FALSE
## sold.fctr.predict.All.X.no.rnorm.rf.error
## 848 -0.068
## 1648 -0.062
## 1007 0.132
## 1746 0.140
## 1560 0.298
## 595 0.460
## UniqueID sold.fctr sold.fctr.predict.All.X.no.rnorm.rf.prob
## 488 10488 N 0.946
## 1622 11622 N 0.958
## 595 10595 N 0.960
## 455 10455 N 0.972
## 1691 11691 N 0.996
## 1471 11471 N 0.998
## sold.fctr.predict.All.X.no.rnorm.rf
## 488 Y
## 1622 Y
## 595 Y
## 455 Y
## 1691 Y
## 1471 Y
## sold.fctr.predict.All.X.no.rnorm.rf.accurate
## 488 FALSE
## 1622 FALSE
## 595 FALSE
## 455 FALSE
## 1691 FALSE
## 1471 FALSE
## sold.fctr.predict.All.X.no.rnorm.rf.error
## 488 0.446
## 1622 0.458
## 595 0.460
## 455 0.472
## 1691 0.496
## 1471 0.498
# gather predictions from models better than MFO.*
#mdl_id <- "Conditional.X.rf"
#mdl_id <- "Conditional.X.cp.0.rpart"
#mdl_id <- "Conditional.X.rpart"
# glb_OOBobs_df <- glb_get_predictions(df=glb_OOBobs_df, mdl_id,
# glb_rsp_var_out)
# print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, mdl_id)],
# glb_OOBobs_df[, glb_rsp_var])$table))
# FN_OOB_ids <- c(4721, 4020, 693, 92)
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# glb_feats_df$id[1:5]])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
write.csv(glb_OOBobs_df[, c(glb_id_var,
grep(glb_rsp_var, names(glb_OOBobs_df), fixed=TRUE, value=TRUE))],
paste0(gsub(".", "_", paste0(glb_out_pfx, glb_sel_mdl_id), fixed=TRUE),
"_OOBobs.csv"), row.names=FALSE)
# print(glb_allobs_df[glb_allobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
# dsp_tbl(Headline.contains="[Ee]bola")
# sum(sel_obs(Headline.contains="[Ee]bola"))
# ftable(xtabs(Popular ~ NewsDesk.fctr, data=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,]))
# xtabs(NewsDesk ~ Popular, #Popular ~ NewsDesk.fctr,
# data=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,],
# exclude=NULL)
# print(mycreate_xtab_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular", "NewsDesk", "SectionName", "SubsectionName")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular", "NewsDesk", "SectionName", "SubsectionName")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,],
# tbl_col_names=c("Popular", "NewsDesk")))
# write.csv(glb_chunks_df, paste0(glb_out_pfx, tail(glb_chunks_df, 1)$label, "_",
# tail(glb_chunks_df, 1)$step_minor, "_chunks1.csv"),
# row.names=FALSE)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 12 fit.models 7 2 109.143 123.66 14.517
## 13 fit.models 7 3 123.661 NA NA if (sum(is.na(glb_allobs_df$D.P.http)) > 0)
stop("fit.models_3: Why is this happening ?")## Warning in is.na(glb_allobs_df$D.P.http): is.na() applied to non-(list or
## vector) of type 'NULL'#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
print(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))## [1] "sold.fctr.predict.All.X.no.rnorm.rf.prob"
## [2] "sold.fctr.predict.All.X.no.rnorm.rf"
## [3] "sold.fctr.predict.All.X.no.rnorm.rf.accurate"for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df,
glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_sel_mdl, glb_sel_mdl_id,
glb_model_type,
file=paste0(glb_out_pfx, "selmdl_dsk.RData"))
#load(paste0(glb_out_pfx, "selmdl_dsk.RData"))
rm(ret_lst)## Warning in rm(ret_lst): object 'ret_lst' not foundreplay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 13 fit.models 7 3 123.661 128.068 4.407
## 14 fit.data.training 8 0 128.069 NA NAStep 8.0: fit data training
#load(paste0(glb_inp_pfx, "dsk.RData"))
if (sum(is.na(glb_allobs_df$D.P.http)) > 0)
stop("fit.data.training_0: Why is this happening ?")## Warning in is.na(glb_allobs_df$D.P.http): is.na() applied to non-(list or
## vector) of type 'NULL'# To create specific models
# glb_fin_mdl_id <- NULL; glb_fin_mdl <- NULL;
# glb_sel_mdl_id <- "Conditional.X.cp.0.rpart";
# glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]]; print(glb_sel_mdl)
if (!is.null(glb_fin_mdl_id) && (glb_fin_mdl_id %in% names(glb_models_lst))) {
warning("Final model same as user selected model")
glb_fin_mdl <- glb_sel_mdl
} else {
# print(mdl_feats_df <- myextract_mdl_feats(sel_mdl=glb_sel_mdl,
# entity_df=glb_fitobs_df))
if ((model_method <- glb_sel_mdl$method) == "custom")
# get actual method from the model_id
model_method <- tail(unlist(strsplit(glb_sel_mdl_id, "[.]")), 1)
tune_finmdl_df <- NULL
if (nrow(glb_sel_mdl$bestTune) > 0) {
for (param in names(glb_sel_mdl$bestTune)) {
#print(sprintf("param: %s", param))
if (glb_sel_mdl$bestTune[1, param] != "none")
tune_finmdl_df <- rbind(tune_finmdl_df,
data.frame(parameter=param,
min=glb_sel_mdl$bestTune[1, param],
max=glb_sel_mdl$bestTune[1, param],
by=1)) # by val does not matter
}
}
# Sync with parameters in mydsutils.R
require(gdata)
ret_lst <- myfit_mdl(model_id="Final", model_method=model_method,
indep_vars_vctr=trim(unlist(strsplit(glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"feats"], "[,]"))),
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnobs_df, OOB_df=NULL,
n_cv_folds=glb_n_cv_folds, tune_models_df=tune_finmdl_df,
# Automate from here
# Issues if glb_sel_mdl$method == "rf" b/c trainControl is "oob"; not "cv"
model_loss_mtrx=glb_model_metric_terms,
model_summaryFunction=glb_sel_mdl$control$summaryFunction,
model_metric=glb_sel_mdl$metric,
model_metric_maximize=glb_sel_mdl$maximize)
glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]]
glb_fin_mdl_id <- glb_models_df[length(glb_models_lst), "model_id"]
}## Loading required package: gdata
## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
##
## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
##
## Attaching package: 'gdata'
##
## The following object is masked from 'package:randomForest':
##
## combine
##
## The following objects are masked from 'package:dplyr':
##
## combine, first, last
##
## The following object is masked from 'package:stats':
##
## nobs
##
## The following object is masked from 'package:utils':
##
## object.size## [1] "fitting model: Final.rf"
## [1] " indep_vars: biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.P.air, D.T.use, storage.fctr, D.P.mini, D.T.condition, D.npnct11.log, D.T.ipad, D.npnct10.log, D.sum.TfIdf, D.T.new, D.npnct13.log, D.npnct08.log, D.T.condit, color.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.nwrds.unq.log, D.ndgts.log, carrier.fctr, cellular.fctr, D.npnct14.log, prdline.my.fctr, D.npnct05.log, condition.fctr, startprice.log"
## Aggregating results
## Fitting final model on full training set
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 1861 factor numeric
## err.rate 1500 -none- numeric
## confusion 6 -none- numeric
## votes 3722 matrix numeric
## oob.times 1861 -none- numeric
## classes 2 -none- character
## importance 71 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 1861 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 71 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6321205
## 2 0.1 0.8237548
## 3 0.2 0.9302326
## 4 0.3 0.9668353
## 5 0.4 0.9878963
## 6 0.5 0.9906977
## 7 0.6 0.9799054
## 8 0.7 0.9284381
## 9 0.8 0.8504673
## 10 0.9 0.7436085
## 11 1.0 0.1682641## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Final.rf.N sold.fctr.predict.Final.rf.Y
## 1 N 993 8
## 2 Y 8 852
## Prediction
## Reference N Y
## N 993 8
## Y 8 852
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.9914025 0.9827057 0.9860756 0.9950780 0.5378829
## AccuracyPValue McnemarPValue
## 0.0000000 1.0000000## Warning in mypredict_mdl(mdl, df = fit_df, rsp_var, rsp_var_out,
## model_id_method, : Expecting 1 metric: Accuracy; recd: Accuracy, Kappa;
## retaining Accuracy only
## model_id model_method
## 1 Final.rf rf
## feats
## 1 biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.T.screen, D.npnct03.log, D.T.used, D.T.excellent, D.T.great, D.ratio.sum.TfIdf.nwrds, D.T.good, D.npnct01.log, D.T.veri, D.T.work, D.T.scratch, D.P.air, D.T.use, storage.fctr, D.P.mini, D.T.condition, D.npnct11.log, D.T.ipad, D.npnct10.log, D.sum.TfIdf, D.T.new, D.npnct13.log, D.npnct08.log, D.T.condit, color.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.T.this, D.npnct06.log, D.T.box, D.npnct28.log, D.T.like, D.npnct12.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct09.log, D.nwrds.unq.log, D.ndgts.log, carrier.fctr, cellular.fctr, D.npnct14.log, prdline.my.fctr, D.npnct05.log, condition.fctr, startprice.log
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 17.888 9.921
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9993239 0.5 0.9906977 0.8022484
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.9860756 0.995078 0.5986616
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01547911 0.03027376rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 14 fit.data.training 8 0 128.069 150.44 22.371
## 15 fit.data.training 8 1 150.441 NA NA#```
#```{r fit.data.training_1, cache=FALSE}
glb_trnobs_df <- glb_get_predictions(df=glb_trnobs_df, mdl_id=glb_fin_mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id, "opt.prob.threshold.OOB"], NULL))## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## glb_fin_mdl_id, : Using default probability threshold: 0.5sav_featsimp_df <- glb_featsimp_df
#glb_feats_df <- sav_feats_df
# glb_feats_df <- mymerge_feats_importance(feats_df=glb_feats_df, sel_mdl=glb_fin_mdl,
# entity_df=glb_trnobs_df)
glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl, featsimp_df=glb_featsimp_df)
glb_featsimp_df[, paste0(glb_fin_mdl_id, ".importance")] <- glb_featsimp_df$importance
print(glb_featsimp_df)## All.X.no.rnorm.rf.importance
## startprice.log 1.000000e+02
## biddable 6.585711e+01
## D.sum.TfIdf 6.599497e+00
## D.ratio.sum.TfIdf.nwrds 6.017068e+00
## prdline.my.fctriPadAir 7.742557e+00
## D.ratio.nstopwrds.nwrds 5.141973e+00
## color.fctrWhite 4.566188e+00
## prdline.my.fctriPad 3+ 5.017083e+00
## condition.fctrFor parts or not working 5.031585e+00
## cellular.fctr1 3.542109e+00
## color.fctrBlack 4.674836e+00
## storage.fctr16 4.035228e+00
## condition.fctrNew 4.081109e+00
## prdline.my.fctriPadmini 4.262759e+00
## D.nchrs.log 3.322331e+00
## storage.fctr64 3.647991e+00
## color.fctrSpace Gray 3.723290e+00
## prdline.my.fctriPad 2 3.313279e+00
## D.nuppr.log 3.298557e+00
## D.nstopwrds.log 2.682286e+00
## prdline.my.fctriPadmini 2+ 2.492035e+00
## D.nwrds.log 2.820643e+00
## carrier.fctrUnknown 2.697432e+00
## prdline.my.fctrUnknown 3.666784e+00
## storage.fctr32 3.473306e+00
## D.nwrds.unq.log 3.111615e+00
## carrier.fctrAT&T 2.494405e+00
## storage.fctrUnknown 2.006601e+00
## cellular.fctrUnknown 2.330567e+00
## carrier.fctrVerizon 2.284560e+00
## D.T.condition 1.870207e+00
## D.T.use 2.012339e+00
## D.T.scratch 1.492880e+00
## condition.fctrSeller refurbished 1.961753e+00
## condition.fctrNew other (see details) 1.864832e+00
## D.npnct13.log 1.895144e+00
## D.T.screen 1.078978e+00
## D.npnct11.log 1.456955e+00
## D.T.condit 1.181293e+00
## D.T.veri 1.622926e+00
## D.T.great 1.092352e+00
## D.T.ipad 1.298498e+00
## color.fctrGold 1.764521e+00
## D.T.work 1.256848e+00
## condition.fctrManufacturer refurbished 7.709178e-01
## D.npnct15.log 7.862747e-01
## D.ndgts.log 7.008568e-01
## D.T.good 1.179454e+00
## D.T.this 7.472430e-01
## D.T.like 4.947486e-01
## carrier.fctrSprint 6.205520e-01
## carrier.fctrT-Mobile 9.054299e-01
## D.T.excellent 4.858415e-01
## D.T.used 5.217063e-01
## D.T.box 7.567341e-01
## D.T.new 5.602444e-01
## D.npnct08.log 4.418547e-01
## D.npnct24.log 5.812858e-01
## D.npnct05.log 2.422318e-01
## D.npnct16.log 3.276553e-01
## D.npnct12.log 2.524313e-01
## D.npnct01.log 2.657222e-01
## D.npnct10.log 5.572065e-01
## D.npnct14.log 6.508058e-01
## D.npnct06.log 1.910556e-01
## D.P.mini 6.911667e-02
## D.npnct03.log 2.476569e-01
## D.P.air 3.743295e-02
## carrier.fctrOther 8.536654e-02
## D.npnct09.log 0.000000e+00
## D.npnct28.log 2.020533e-03
## importance Final.rf.importance
## startprice.log 100.00000000 100.00000000
## biddable 84.01086694 84.01086694
## D.sum.TfIdf 6.84972971 6.84972971
## D.ratio.sum.TfIdf.nwrds 6.58291967 6.58291967
## prdline.my.fctriPadAir 6.11333778 6.11333778
## D.ratio.nstopwrds.nwrds 5.34186639 5.34186639
## color.fctrWhite 5.03609010 5.03609010
## prdline.my.fctriPad 3+ 4.66566996 4.66566996
## condition.fctrFor parts or not working 4.47304451 4.47304451
## cellular.fctr1 4.46209243 4.46209243
## color.fctrBlack 4.44173876 4.44173876
## storage.fctr16 4.43555428 4.43555428
## condition.fctrNew 4.39306734 4.39306734
## prdline.my.fctriPadmini 4.33134890 4.33134890
## D.nchrs.log 4.23124296 4.23124296
## storage.fctr64 4.16841815 4.16841815
## color.fctrSpace Gray 4.15082479 4.15082479
## prdline.my.fctriPad 2 3.98897970 3.98897970
## D.nuppr.log 3.78214665 3.78214665
## D.nstopwrds.log 3.48684737 3.48684737
## prdline.my.fctriPadmini 2+ 3.24643000 3.24643000
## D.nwrds.log 3.01926125 3.01926125
## carrier.fctrUnknown 2.93083014 2.93083014
## prdline.my.fctrUnknown 2.89215146 2.89215146
## storage.fctr32 2.88961919 2.88961919
## D.nwrds.unq.log 2.83108464 2.83108464
## carrier.fctrAT&T 2.81959917 2.81959917
## storage.fctrUnknown 2.69920218 2.69920218
## cellular.fctrUnknown 2.62954307 2.62954307
## carrier.fctrVerizon 2.46698119 2.46698119
## D.T.condition 2.38146934 2.38146934
## D.T.use 2.34906422 2.34906422
## D.T.scratch 2.08450703 2.08450703
## condition.fctrSeller refurbished 2.06955991 2.06955991
## condition.fctrNew other (see details) 1.78399119 1.78399119
## D.npnct13.log 1.78264318 1.78264318
## D.T.screen 1.77068810 1.77068810
## D.npnct11.log 1.70487109 1.70487109
## D.T.condit 1.63751270 1.63751270
## D.T.veri 1.53194465 1.53194465
## D.T.great 1.48534343 1.48534343
## D.T.ipad 1.44131373 1.44131373
## color.fctrGold 1.44059605 1.44059605
## D.T.work 1.36081008 1.36081008
## condition.fctrManufacturer refurbished 1.35202620 1.35202620
## D.npnct15.log 1.29918286 1.29918286
## D.ndgts.log 1.28828445 1.28828445
## D.T.good 1.26079945 1.26079945
## D.T.this 1.05599984 1.05599984
## D.T.like 1.04430315 1.04430315
## carrier.fctrSprint 0.98361766 0.98361766
## carrier.fctrT-Mobile 0.86749752 0.86749752
## D.T.excellent 0.81061695 0.81061695
## D.T.used 0.72390213 0.72390213
## D.T.box 0.68547640 0.68547640
## D.T.new 0.63346436 0.63346436
## D.npnct08.log 0.51792407 0.51792407
## D.npnct24.log 0.43799623 0.43799623
## D.npnct05.log 0.42477270 0.42477270
## D.npnct16.log 0.42260999 0.42260999
## D.npnct12.log 0.40204791 0.40204791
## D.npnct01.log 0.39998744 0.39998744
## D.npnct10.log 0.39161798 0.39161798
## D.npnct14.log 0.31682709 0.31682709
## D.npnct06.log 0.26839184 0.26839184
## D.P.mini 0.25541979 0.25541979
## D.npnct03.log 0.10595867 0.10595867
## D.P.air 0.07155810 0.07155810
## carrier.fctrOther 0.06320552 0.06320552
## D.npnct09.log 0.01547150 0.01547150
## D.npnct28.log 0.00000000 0.00000000if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_trnobs_df, mdl_id =
## glb_fin_mdl_id, : Limiting important feature scatter plots to 5 out of 50
## [1] "Min/Max Boundaries: "
## UniqueID sold.fctr sold.fctr.predict.Final.rf.prob
## 1351 11351 Y 0.364
## 1061 11061 Y 0.384
## 3 10003 Y 0.440
## 41 10041 Y 0.456
## 1212 11212 Y 0.474
## 1 10001 N 0.218
## 2 10002 Y 1.000
## 91 10091 Y 1.000
## 1397 11397 N 0.008
## 747 10747 N 0.552
## 1674 11674 N 0.574
## 217 10217 N 0.580
## 296 10296 N 0.612
## sold.fctr.predict.Final.rf sold.fctr.predict.Final.rf.accurate
## 1351 N FALSE
## 1061 N FALSE
## 3 N FALSE
## 41 N FALSE
## 1212 N FALSE
## 1 N TRUE
## 2 Y TRUE
## 91 Y TRUE
## 1397 N TRUE
## 747 Y FALSE
## 1674 Y FALSE
## 217 Y FALSE
## 296 Y FALSE
## sold.fctr.predict.Final.rf.error .label
## 1351 -0.136 11351
## 1061 -0.116 11061
## 3 -0.060 10003
## 41 -0.044 10041
## 1212 -0.026 11212
## 1 0.000 10001
## 2 0.000 10002
## 91 0.000 10091
## 1397 0.000 11397
## 747 0.052 10747
## 1674 0.074 11674
## 217 0.080 10217
## 296 0.112 10296
## [1] "Inaccurate: "
## UniqueID sold.fctr sold.fctr.predict.Final.rf.prob
## 1382 11382 Y 0.358
## 1351 11351 Y 0.364
## 1061 11061 Y 0.384
## 1590 11590 Y 0.438
## 3 10003 Y 0.440
## 41 10041 Y 0.456
## 1212 11212 Y 0.474
## 1507 11507 Y 0.482
## 747 10747 N 0.552
## 1569 11569 N 0.552
## 1563 11563 N 0.564
## 1674 11674 N 0.574
## 217 10217 N 0.580
## 296 10296 N 0.612
## 1234 11234 N 0.656
## 1844 11844 N 0.856
## sold.fctr.predict.Final.rf sold.fctr.predict.Final.rf.accurate
## 1382 N FALSE
## 1351 N FALSE
## 1061 N FALSE
## 1590 N FALSE
## 3 N FALSE
## 41 N FALSE
## 1212 N FALSE
## 1507 N FALSE
## 747 Y FALSE
## 1569 Y FALSE
## 1563 Y FALSE
## 1674 Y FALSE
## 217 Y FALSE
## 296 Y FALSE
## 1234 Y FALSE
## 1844 Y FALSE
## sold.fctr.predict.Final.rf.error
## 1382 -0.142
## 1351 -0.136
## 1061 -0.116
## 1590 -0.062
## 3 -0.060
## 41 -0.044
## 1212 -0.026
## 1507 -0.018
## 747 0.052
## 1569 0.052
## 1563 0.064
## 1674 0.074
## 217 0.080
## 296 0.112
## 1234 0.156
## 1844 0.356
dsp_feats_vctr <- c(NULL)
for(var in grep(".importance", names(glb_feats_df), fixed=TRUE, value=TRUE))
dsp_feats_vctr <- union(dsp_feats_vctr,
glb_feats_df[!is.na(glb_feats_df[, var]), "id"])
# print(glb_trnobs_df[glb_trnobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_trnobs_df), value=TRUE)])
print(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))## [1] "sold.fctr.predict.Final.rf.prob" "sold.fctr.predict.Final.rf"for (col in setdiff(names(glb_trnobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.src == "Train", col] <- glb_trnobs_df[, col]
print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))## character(0)for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df, glb_allobs_df,
#glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
## 3.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: data.training.all.prediction
## 4.0000 5 0 1 1 1
## 4.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: model.final
## 5.0000 4 0 0 2 1
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 15 fit.data.training 8 1 150.441 155.805 5.364
## 16 predict.data.new 9 0 155.805 NA NAStep 9.0: predict data new
# Compute final model predictions
# sav_newobs_df <- glb_newobs_df
glb_newobs_df <- glb_get_predictions(glb_newobs_df, mdl_id=glb_fin_mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"], NULL))## Warning in glb_get_predictions(glb_newobs_df, mdl_id = glb_fin_mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.5if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_newobs_df, mdl_id=glb_fin_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_newobs_df, mdl_id=glb_fin_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_newobs_df, mdl_id =
## glb_fin_mdl_id, : Limiting important feature scatter plots to 5 out of 50## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning: Removed 798 rows containing missing values (geom_point).## Warning: Removed 798 rows containing missing values (geom_point).
## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning: Removed 798 rows containing missing values (geom_point).## Warning: Removed 798 rows containing missing values (geom_point).
## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning: Removed 798 rows containing missing values (geom_point).## Warning: Removed 798 rows containing missing values (geom_point).
## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning: Removed 798 rows containing missing values (geom_point).## Warning: Removed 798 rows containing missing values (geom_point).
## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning: Removed 798 rows containing missing values (geom_point).## Warning: Removed 798 rows containing missing values (geom_point).
## [1] "Min/Max Boundaries: "
## UniqueID sold.fctr sold.fctr.predict.Final.rf.prob
## 1862 11862 <NA> 0.268
## 1865 11865 <NA> 0.774
## 1891 11891 <NA> 0.994
## 2625 12625 <NA> 0.286
## sold.fctr.predict.Final.rf sold.fctr.predict.Final.rf.accurate
## 1862 N NA
## 1865 Y NA
## 1891 Y NA
## 2625 N NA
## sold.fctr.predict.Final.rf.error .label
## 1862 0 11862
## 1865 0 11865
## 1891 0 11891
## 2625 0 12625
## [1] "Inaccurate: "
## UniqueID sold.fctr sold.fctr.predict.Final.rf.prob
## NA NA <NA> NA
## NA.1 NA <NA> NA
## NA.2 NA <NA> NA
## NA.3 NA <NA> NA
## NA.4 NA <NA> NA
## NA.5 NA <NA> NA
## sold.fctr.predict.Final.rf sold.fctr.predict.Final.rf.accurate
## NA <NA> NA
## NA.1 <NA> NA
## NA.2 <NA> NA
## NA.3 <NA> NA
## NA.4 <NA> NA
## NA.5 <NA> NA
## sold.fctr.predict.Final.rf.error
## NA NA
## NA.1 NA
## NA.2 NA
## NA.3 NA
## NA.4 NA
## NA.5 NA
## UniqueID sold.fctr sold.fctr.predict.Final.rf.prob
## NA.297 NA <NA> NA
## NA.424 NA <NA> NA
## NA.446 NA <NA> NA
## NA.493 NA <NA> NA
## NA.591 NA <NA> NA
## NA.739 NA <NA> NA
## sold.fctr.predict.Final.rf sold.fctr.predict.Final.rf.accurate
## NA.297 <NA> NA
## NA.424 <NA> NA
## NA.446 <NA> NA
## NA.493 <NA> NA
## NA.591 <NA> NA
## NA.739 <NA> NA
## sold.fctr.predict.Final.rf.error
## NA.297 NA
## NA.424 NA
## NA.446 NA
## NA.493 NA
## NA.591 NA
## NA.739 NA
## UniqueID sold.fctr sold.fctr.predict.Final.rf.prob
## NA.792 NA <NA> NA
## NA.793 NA <NA> NA
## NA.794 NA <NA> NA
## NA.795 NA <NA> NA
## NA.796 NA <NA> NA
## NA.797 NA <NA> NA
## sold.fctr.predict.Final.rf sold.fctr.predict.Final.rf.accurate
## NA.792 <NA> NA
## NA.793 <NA> NA
## NA.794 <NA> NA
## NA.795 <NA> NA
## NA.796 <NA> NA
## NA.797 <NA> NA
## sold.fctr.predict.Final.rf.error
## NA.792 NA
## NA.793 NA
## NA.794 NA
## NA.795 NA
## NA.796 NA
## NA.797 NA## Warning: Removed 798 rows containing missing values (geom_point).
if (glb_is_classification && glb_is_binomial) {
submit_df <- glb_newobs_df[, c(glb_id_var,
paste0(glb_rsp_var_out, glb_fin_mdl_id, ".prob"))]
names(submit_df)[2] <- "Probability1"
# submit_df <- glb_newobs_df[, c(paste0(glb_rsp_var_out, glb_fin_mdl_id)), FALSE]
# names(submit_df)[1] <- "BDscience"
# submit_df$BDscience <- as.numeric(submit_df$BDscience) - 1
# #submit_df <-rbind(submit_df, data.frame(bdanalytics=c(" ")))
# print("Submission Stats:")
# print(table(submit_df$BDscience, useNA = "ifany"))
} else submit_df <- glb_newobs_df[, c(glb_id_var,
paste0(glb_rsp_var_out, glb_fin_mdl_id))]
submit_fname <- paste0(gsub(".", "_", paste0(glb_out_pfx, glb_fin_mdl_id), fixed=TRUE),
"_submit.csv")
write.csv(submit_df, submit_fname, quote=FALSE, row.names=FALSE)
#cat(" ", "\n", file=submit_fn, append=TRUE)
# print(orderBy(~ -max.auc.OOB, glb_models_df[, c("model_id",
# "max.auc.OOB", "max.Accuracy.OOB")]))
if (glb_is_classification && glb_is_binomial)
print(glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"])## [1] 0.5print(sprintf("glb_sel_mdl_id: %s", glb_sel_mdl_id))## [1] "glb_sel_mdl_id: All.X.no.rnorm.rf"print(sprintf("glb_fin_mdl_id: %s", glb_fin_mdl_id))## [1] "glb_fin_mdl_id: Final.rf"print(dim(glb_fitobs_df))## [1] 976 64print(dsp_models_df)## model_id max.Accuracy.OOB max.auc.OOB max.Kappa.OOB
## 12 All.X.no.rnorm.rf 0.7977401 0.8503935 0.5911080
## 6 Max.cor.Y.glm 0.7932203 0.8357056 0.5818261
## 8 Low.cor.X.glm 0.7932203 0.8289074 0.5812465
## 10 All.X.bayesglm 0.7909605 0.8305151 0.5772559
## 5 Max.cor.Y.rpart 0.7864407 0.7844353 0.5697529
## 11 All.X.no.rnorm.rpart 0.7864407 0.7844353 0.5697529
## 9 All.X.glm 0.7830508 0.8281780 0.5614877
## 7 Interact.High.cor.Y.glm 0.7807910 0.8370000 0.5596608
## 4 Max.cor.Y.cv.0.cp.0.rpart 0.7774011 0.8257253 0.5503014
## 1 MFO.myMFO_classfr 0.5378531 0.5000000 0.0000000
## 3 Max.cor.Y.cv.0.rpart 0.5378531 0.5000000 0.0000000
## 2 Random.myrandom_classfr 0.4621469 0.5214656 0.0000000
## min.aic.fit opt.prob.threshold.OOB
## 12 NA 0.5
## 6 942.0369 0.5
## 8 963.5142 0.5
## 10 982.9554 0.5
## 5 NA 0.7
## 11 NA 0.7
## 9 976.6992 0.5
## 7 950.5986 0.4
## 4 NA 0.5
## 1 NA 0.5
## 3 NA 0.5
## 2 NA 0.4if (glb_is_regression) {
print(sprintf("%s OOB RMSE: %0.4f", glb_sel_mdl_id,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id, "min.RMSE.OOB"]))
if (!is.null(glb_category_vars)) {
stop("not implemented yet")
tmp_OOBobs_df <- glb_OOBobs_df[, c(glb_category_vars, predct_accurate_var_name)]
names(tmp_OOBobs_df)[length(names(tmp_OOBobs_df))] <- "accurate.OOB"
aOOB_ctgry_df <- mycreate_xtab_df(tmp_OOBobs_df, names(tmp_OOBobs_df))
aOOB_ctgry_df[is.na(aOOB_ctgry_df)] <- 0
aOOB_ctgry_df <- mutate(aOOB_ctgry_df,
.n.OOB = accurate.OOB.FALSE + accurate.OOB.TRUE,
max.accuracy.OOB = accurate.OOB.TRUE / .n.OOB)
#intersect(names(glb_ctgry_df), names(aOOB_ctgry_df))
glb_ctgry_df <- merge(glb_ctgry_df, aOOB_ctgry_df, all=TRUE)
print(orderBy(~-accurate.OOB.FALSE, glb_ctgry_df))
}
if ((glb_rsp_var %in% names(glb_newobs_df)) &&
!(any(is.na(glb_newobs_df[, glb_rsp_var])))) {
pred_stats_df <-
mypredict_mdl(mdl=glb_models_lst[[glb_fin_mdl_id]],
df=glb_newobs_df,
rsp_var=glb_rsp_var,
rsp_var_out=glb_rsp_var_out,
model_id_method=glb_fin_mdl_id,
label="new",
model_summaryFunction=glb_sel_mdl$control$summaryFunction,
model_metric=glb_sel_mdl$metric,
model_metric_maximize=glb_sel_mdl$maximize,
ret_type="stats")
print(sprintf("%s prediction stats for glb_newobs_df:", glb_fin_mdl_id))
print(pred_stats_df)
}
}
if (glb_is_classification) {
print(sprintf("%s OOB confusion matrix & accuracy: ", glb_sel_mdl_id))
print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)],
glb_OOBobs_df[, glb_rsp_var])$table))
if (!is.null(glb_category_vars)) {
tmp_OOBobs_df <- glb_OOBobs_df[, c(glb_category_vars, predct_accurate_var_name)]
names(tmp_OOBobs_df)[length(names(tmp_OOBobs_df))] <- "accurate.OOB"
aOOB_ctgry_df <- mycreate_xtab_df(tmp_OOBobs_df, names(tmp_OOBobs_df))
aOOB_ctgry_df[is.na(aOOB_ctgry_df)] <- 0
aOOB_ctgry_df <- mutate(aOOB_ctgry_df,
.n.OOB = accurate.OOB.FALSE + accurate.OOB.TRUE,
max.accuracy.OOB = accurate.OOB.TRUE / .n.OOB)
#intersect(names(glb_ctgry_df), names(aOOB_ctgry_df))
glb_ctgry_df <- merge(glb_ctgry_df, aOOB_ctgry_df, all=TRUE)
print(orderBy(~-accurate.OOB.FALSE, glb_ctgry_df))
}
if ((glb_rsp_var %in% names(glb_newobs_df)) &&
!(any(is.na(glb_newobs_df[, glb_rsp_var])))) {
print(sprintf("%s new confusion matrix & accuracy: ", glb_fin_mdl_id))
print(t(confusionMatrix(glb_newobs_df[, paste0(glb_rsp_var_out, glb_fin_mdl_id)],
glb_newobs_df[, glb_rsp_var])$table))
}
} ## [1] "All.X.no.rnorm.rf OOB confusion matrix & accuracy: "
## Prediction
## Reference N Y
## N 401 75
## Y 104 305
## prdline.my .n.OOB .n.Tst .freqRatio.Tst .freqRatio.OOB
## 4 iPadAir 152 137 0.1716792 0.1717514
## 2 iPad 2 171 154 0.1929825 0.1932203
## 5 iPadmini 126 114 0.1428571 0.1423729
## 6 iPadmini 2+ 104 94 0.1177945 0.1175141
## 3 iPad 3+ 136 123 0.1541353 0.1536723
## 1 iPad 1 99 89 0.1115288 0.1118644
## 7 Unknown 97 87 0.1090226 0.1096045
## accurate.OOB.FALSE accurate.OOB.TRUE max.accuracy.OOB
## 4 34 118 0.7763158
## 2 28 143 0.8362573
## 5 26 100 0.7936508
## 6 26 78 0.7500000
## 3 25 111 0.8161765
## 1 20 79 0.7979798
## 7 20 77 0.7938144dsp_myCategory_conf_mtrx <- function(myCategory) {
print(sprintf("%s OOB::myCategory=%s confusion matrix & accuracy: ",
glb_sel_mdl_id, myCategory))
print(t(confusionMatrix(
glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory,
paste0(glb_rsp_var_out, glb_sel_mdl_id)],
glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory, glb_rsp_var])$table))
print(sum(glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory,
predct_accurate_var_name]) /
nrow(glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory, ]))
err_ids <- glb_OOBobs_df[(glb_OOBobs_df$myCategory == myCategory) &
(!glb_OOBobs_df[, predct_accurate_var_name]), glb_id_var]
OOB_FNerr_df <- glb_OOBobs_df[(glb_OOBobs_df$UniqueID %in% err_ids) &
(glb_OOBobs_df$Popular == 1),
c(
".clusterid",
"Popular", "Headline", "Snippet", "Abstract")]
print(sprintf("%s OOB::myCategory=%s FN errors: %d", glb_sel_mdl_id, myCategory,
nrow(OOB_FNerr_df)))
print(OOB_FNerr_df)
OOB_FPerr_df <- glb_OOBobs_df[(glb_OOBobs_df$UniqueID %in% err_ids) &
(glb_OOBobs_df$Popular == 0),
c(
".clusterid",
"Popular", "Headline", "Snippet", "Abstract")]
print(sprintf("%s OOB::myCategory=%s FP errors: %d", glb_sel_mdl_id, myCategory,
nrow(OOB_FPerr_df)))
print(OOB_FPerr_df)
}
#dsp_myCategory_conf_mtrx(myCategory="OpEd#Opinion#")
#dsp_myCategory_conf_mtrx(myCategory="Business#Business Day#Dealbook")
#dsp_myCategory_conf_mtrx(myCategory="##")
# if (glb_is_classification) {
# print("FN_OOB_ids:")
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
# print(dsp_vctr <- colSums(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# setdiff(grep("[HSA].", names(glb_OOBobs_df), value=TRUE),
# union(myfind_chr_cols_df(glb_OOBobs_df),
# grep(".fctr", names(glb_OOBobs_df), fixed=TRUE, value=TRUE)))]))
# }
dsp_hdlpfx_results <- function(hdlpfx) {
print(hdlpfx)
print(glb_OOBobs_df[glb_OOBobs_df$Headline.pfx %in% c(hdlpfx),
grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
print(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
grep(glb_rsp_var, names(glb_newobs_df), value=TRUE)])
print(dsp_vctr <- colSums(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
setdiff(grep("[HSA]\\.", names(glb_newobs_df), value=TRUE),
union(myfind_chr_cols_df(glb_newobs_df),
grep(".fctr", names(glb_newobs_df), fixed=TRUE, value=TRUE)))]))
print(dsp_vctr <- dsp_vctr[dsp_vctr != 0])
print(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
union(names(dsp_vctr), myfind_chr_cols_df(glb_newobs_df))])
}
#dsp_hdlpfx_results(hdlpfx="Ask Well::")
# print("myMisc::|OpEd|blank|blank|1:")
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% c(6446),
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# c("WordCount", "WordCount.log", "myMultimedia",
# "NewsDesk", "SectionName", "SubsectionName")])
# print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains="[Vv]ideo"), ],
# c(glb_rsp_var, "myMultimedia")))
# dsp_chisq.test(Headline.contains="[Vi]deo")
# print(glb_allobs_df[sel_obs(Headline.contains="[Vv]ideo"),
# c(glb_rsp_var, "Popular", "myMultimedia", "Headline")])
# print(glb_allobs_df[sel_obs(Headline.contains="[Ee]bola", Popular=1),
# c(glb_rsp_var, "Popular", "myMultimedia", "Headline",
# "NewsDesk", "SectionName", "SubsectionName")])
# print(subset(glb_feats_df, !is.na(importance))[,
# c("is.ConditionalX.y",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, is.ConditionalX.y & is.na(importance))[,
# c("is.ConditionalX.y",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, !is.na(importance))[,
# c("zeroVar", "nzv", "myNearZV",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, is.na(importance))[,
# c("zeroVar", "nzv", "myNearZV",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
print(orderBy(as.formula(paste0("~ -", glb_sel_mdl_id, ".importance")), glb_featsimp_df))## All.X.no.rnorm.rf.importance
## startprice.log 1.000000e+02
## biddable 6.585711e+01
## prdline.my.fctriPadAir 7.742557e+00
## D.sum.TfIdf 6.599497e+00
## D.ratio.sum.TfIdf.nwrds 6.017068e+00
## D.ratio.nstopwrds.nwrds 5.141973e+00
## condition.fctrFor parts or not working 5.031585e+00
## prdline.my.fctriPad 3+ 5.017083e+00
## color.fctrBlack 4.674836e+00
## color.fctrWhite 4.566188e+00
## prdline.my.fctriPadmini 4.262759e+00
## condition.fctrNew 4.081109e+00
## storage.fctr16 4.035228e+00
## color.fctrSpace Gray 3.723290e+00
## prdline.my.fctrUnknown 3.666784e+00
## storage.fctr64 3.647991e+00
## cellular.fctr1 3.542109e+00
## storage.fctr32 3.473306e+00
## D.nchrs.log 3.322331e+00
## prdline.my.fctriPad 2 3.313279e+00
## D.nuppr.log 3.298557e+00
## D.nwrds.unq.log 3.111615e+00
## D.nwrds.log 2.820643e+00
## carrier.fctrUnknown 2.697432e+00
## D.nstopwrds.log 2.682286e+00
## carrier.fctrAT&T 2.494405e+00
## prdline.my.fctriPadmini 2+ 2.492035e+00
## cellular.fctrUnknown 2.330567e+00
## carrier.fctrVerizon 2.284560e+00
## D.T.use 2.012339e+00
## storage.fctrUnknown 2.006601e+00
## condition.fctrSeller refurbished 1.961753e+00
## D.npnct13.log 1.895144e+00
## D.T.condition 1.870207e+00
## condition.fctrNew other (see details) 1.864832e+00
## color.fctrGold 1.764521e+00
## D.T.veri 1.622926e+00
## D.T.scratch 1.492880e+00
## D.npnct11.log 1.456955e+00
## D.T.ipad 1.298498e+00
## D.T.work 1.256848e+00
## D.T.condit 1.181293e+00
## D.T.good 1.179454e+00
## D.T.great 1.092352e+00
## D.T.screen 1.078978e+00
## carrier.fctrT-Mobile 9.054299e-01
## D.npnct15.log 7.862747e-01
## condition.fctrManufacturer refurbished 7.709178e-01
## D.T.box 7.567341e-01
## D.T.this 7.472430e-01
## D.ndgts.log 7.008568e-01
## D.npnct14.log 6.508058e-01
## carrier.fctrSprint 6.205520e-01
## D.npnct24.log 5.812858e-01
## D.T.new 5.602444e-01
## D.npnct10.log 5.572065e-01
## D.T.used 5.217063e-01
## D.T.like 4.947486e-01
## D.T.excellent 4.858415e-01
## D.npnct08.log 4.418547e-01
## D.npnct16.log 3.276553e-01
## D.npnct01.log 2.657222e-01
## D.npnct12.log 2.524313e-01
## D.npnct03.log 2.476569e-01
## D.npnct05.log 2.422318e-01
## D.npnct06.log 1.910556e-01
## carrier.fctrOther 8.536654e-02
## D.P.mini 6.911667e-02
## D.P.air 3.743295e-02
## D.npnct28.log 2.020533e-03
## D.npnct09.log 0.000000e+00
## importance Final.rf.importance
## startprice.log 100.00000000 100.00000000
## biddable 84.01086694 84.01086694
## prdline.my.fctriPadAir 6.11333778 6.11333778
## D.sum.TfIdf 6.84972971 6.84972971
## D.ratio.sum.TfIdf.nwrds 6.58291967 6.58291967
## D.ratio.nstopwrds.nwrds 5.34186639 5.34186639
## condition.fctrFor parts or not working 4.47304451 4.47304451
## prdline.my.fctriPad 3+ 4.66566996 4.66566996
## color.fctrBlack 4.44173876 4.44173876
## color.fctrWhite 5.03609010 5.03609010
## prdline.my.fctriPadmini 4.33134890 4.33134890
## condition.fctrNew 4.39306734 4.39306734
## storage.fctr16 4.43555428 4.43555428
## color.fctrSpace Gray 4.15082479 4.15082479
## prdline.my.fctrUnknown 2.89215146 2.89215146
## storage.fctr64 4.16841815 4.16841815
## cellular.fctr1 4.46209243 4.46209243
## storage.fctr32 2.88961919 2.88961919
## D.nchrs.log 4.23124296 4.23124296
## prdline.my.fctriPad 2 3.98897970 3.98897970
## D.nuppr.log 3.78214665 3.78214665
## D.nwrds.unq.log 2.83108464 2.83108464
## D.nwrds.log 3.01926125 3.01926125
## carrier.fctrUnknown 2.93083014 2.93083014
## D.nstopwrds.log 3.48684737 3.48684737
## carrier.fctrAT&T 2.81959917 2.81959917
## prdline.my.fctriPadmini 2+ 3.24643000 3.24643000
## cellular.fctrUnknown 2.62954307 2.62954307
## carrier.fctrVerizon 2.46698119 2.46698119
## D.T.use 2.34906422 2.34906422
## storage.fctrUnknown 2.69920218 2.69920218
## condition.fctrSeller refurbished 2.06955991 2.06955991
## D.npnct13.log 1.78264318 1.78264318
## D.T.condition 2.38146934 2.38146934
## condition.fctrNew other (see details) 1.78399119 1.78399119
## color.fctrGold 1.44059605 1.44059605
## D.T.veri 1.53194465 1.53194465
## D.T.scratch 2.08450703 2.08450703
## D.npnct11.log 1.70487109 1.70487109
## D.T.ipad 1.44131373 1.44131373
## D.T.work 1.36081008 1.36081008
## D.T.condit 1.63751270 1.63751270
## D.T.good 1.26079945 1.26079945
## D.T.great 1.48534343 1.48534343
## D.T.screen 1.77068810 1.77068810
## carrier.fctrT-Mobile 0.86749752 0.86749752
## D.npnct15.log 1.29918286 1.29918286
## condition.fctrManufacturer refurbished 1.35202620 1.35202620
## D.T.box 0.68547640 0.68547640
## D.T.this 1.05599984 1.05599984
## D.ndgts.log 1.28828445 1.28828445
## D.npnct14.log 0.31682709 0.31682709
## carrier.fctrSprint 0.98361766 0.98361766
## D.npnct24.log 0.43799623 0.43799623
## D.T.new 0.63346436 0.63346436
## D.npnct10.log 0.39161798 0.39161798
## D.T.used 0.72390213 0.72390213
## D.T.like 1.04430315 1.04430315
## D.T.excellent 0.81061695 0.81061695
## D.npnct08.log 0.51792407 0.51792407
## D.npnct16.log 0.42260999 0.42260999
## D.npnct01.log 0.39998744 0.39998744
## D.npnct12.log 0.40204791 0.40204791
## D.npnct03.log 0.10595867 0.10595867
## D.npnct05.log 0.42477270 0.42477270
## D.npnct06.log 0.26839184 0.26839184
## carrier.fctrOther 0.06320552 0.06320552
## D.P.mini 0.25541979 0.25541979
## D.P.air 0.07155810 0.07155810
## D.npnct28.log 0.00000000 0.00000000
## D.npnct09.log 0.01547150 0.01547150print("glb_newobs_df prediction stats:")## [1] "glb_newobs_df prediction stats:"print(myplot_histogram(glb_newobs_df, paste0(glb_rsp_var_out, glb_fin_mdl_id)))
if (glb_is_classification)
print(table(glb_newobs_df[, paste0(glb_rsp_var_out, glb_fin_mdl_id)]))##
## N Y
## 485 313# players_df <- data.frame(id=c("Chavez", "Giambi", "Menechino", "Myers", "Pena"),
# OBP=c(0.338, 0.391, 0.369, 0.313, 0.361),
# SLG=c(0.540, 0.450, 0.374, 0.447, 0.500),
# cost=c(1400000, 1065000, 295000, 800000, 300000))
# players_df$RS.predict <- predict(glb_models_lst[[csm_mdl_id]], players_df)
# print(orderBy(~ -RS.predict, players_df))
if (length(diff <- setdiff(names(glb_trnobs_df), names(glb_allobs_df))) > 0)
print(diff)
for (col in setdiff(names(glb_trnobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.src == "Train", col] <- glb_trnobs_df[, col]
if (length(diff <- setdiff(names(glb_fitobs_df), names(glb_allobs_df))) > 0)
print(diff)
if (length(diff <- setdiff(names(glb_OOBobs_df), names(glb_allobs_df))) > 0)
print(diff)
for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
if (length(diff <- setdiff(names(glb_newobs_df), names(glb_allobs_df))) > 0)
print(diff)
if (glb_save_envir)
save(glb_feats_df, glb_allobs_df,
#glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "prdnew_dsk.RData"))
rm(submit_df, tmp_OOBobs_df)
# tmp_replay_lst <- replay.petrisim(pn=glb_analytics_pn,
# replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
# "data.new.prediction")), flip_coord=TRUE)
# print(ggplot.petrinet(tmp_replay_lst[["pn"]]) + coord_flip())
glb_chunks_df <- myadd_chunk(glb_chunks_df, "display.session.info", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 16 predict.data.new 9 0 155.805 161.756 5.951
## 17 display.session.info 10 0 161.756 NA NANull Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## label step_major step_minor bgn end elapsed
## 11 fit.models 7 1 68.916 109.142 40.226
## 10 fit.models 7 0 42.675 68.915 26.240
## 14 fit.data.training 8 0 128.069 150.440 22.371
## 5 extract.features 3 0 17.722 39.149 21.427
## 12 fit.models 7 2 109.143 123.660 14.517
## 2 inspect.data 2 0 10.197 16.802 6.605
## 16 predict.data.new 9 0 155.805 161.756 5.951
## 15 fit.data.training 8 1 150.441 155.805 5.364
## 13 fit.models 7 3 123.661 128.068 4.407
## 8 select.features 5 0 40.217 41.783 1.566
## 6 cluster.data 4 0 39.150 40.071 0.922
## 9 partition.data.training 6 0 41.784 42.675 0.891
## 3 scrub.data 2 1 16.802 17.632 0.830
## 1 import.data 1 0 9.733 10.197 0.464
## 7 manage.missing.data 4 1 40.072 40.216 0.144
## 4 transform.data 2 2 17.632 17.722 0.090
## duration
## 11 40.226
## 10 26.240
## 14 22.371
## 5 21.427
## 12 14.517
## 2 6.605
## 16 5.951
## 15 5.364
## 13 4.407
## 8 1.566
## 6 0.921
## 9 0.891
## 3 0.830
## 1 0.464
## 7 0.144
## 4 0.090
## [1] "Total Elapsed Time: 161.756 secs"
## R version 3.2.1 (2015-06-18)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.10.4 (Yosemite)
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] tcltk grid parallel stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] gdata_2.17.0 randomForest_4.6-10 arm_1.8-6
## [4] lme4_1.1-8 Matrix_1.2-2 MASS_7.3-42
## [7] rpart.plot_1.5.2 rpart_4.1-10 ROCR_1.0-7
## [10] gplots_2.17.0 sampling_2.7 tm_0.6-2
## [13] NLP_0.1-8 stringr_1.0.0 dplyr_0.4.2
## [16] plyr_1.8.3 sqldf_0.4-10 RSQLite_1.0.0
## [19] DBI_0.3.1 gsubfn_0.6-6 proto_0.3-10
## [22] reshape2_1.4.1 doMC_1.3.3 iterators_1.0.7
## [25] foreach_1.4.2 doBy_4.5-13 survival_2.38-3
## [28] caret_6.0-47 ggplot2_1.0.1 lattice_0.20-33
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.11.6 class_7.3-13 gtools_3.5.0
## [4] assertthat_0.1 digest_0.6.8 slam_0.1-32
## [7] R6_2.1.0 BradleyTerry2_1.0-6 chron_2.3-47
## [10] coda_0.17-1 evaluate_0.7 e1071_1.6-4
## [13] lazyeval_0.1.10 minqa_1.2.4 SparseM_1.6
## [16] car_2.0-25 nloptr_1.0.4 rmarkdown_0.7
## [19] labeling_0.3 splines_3.2.1 munsell_0.4.2
## [22] compiler_3.2.1 mgcv_1.8-6 htmltools_0.2.6
## [25] nnet_7.3-10 lpSolve_5.6.11 codetools_0.2-14
## [28] brglm_0.5-9 bitops_1.0-6 nlme_3.1-121
## [31] gtable_0.1.2 magrittr_1.5 formatR_1.2
## [34] scales_0.2.5 KernSmooth_2.23-15 stringi_0.5-5
## [37] RColorBrewer_1.1-2 tools_3.2.1 abind_1.4-3
## [40] pbkrtest_0.4-2 yaml_2.1.13 colorspace_1.2-6
## [43] caTools_1.17.1 knitr_1.10.5 quantreg_5.11